Overview

Dataset statistics

Number of variables225
Number of observations561184
Missing cells86103972
Missing cells (%)68.2%
Total size in memory963.3 MiB
Average record size in memory1.8 KiB

Variable types

Numeric61
Categorical159
Unsupported5

Alerts

security_pledge has constant value ""Constant
prospectus_issuer_name has a high cardinality: 18920 distinct valuesHigh cardinality
issuer_cusip has a high cardinality: 20621 distinct valuesHigh cardinality
issue_cusip has a high cardinality: 10315 distinct valuesHigh cardinality
issue_name has a high cardinality: 136216 distinct valuesHigh cardinality
maturity has a high cardinality: 16750 distinct valuesHigh cardinality
treasury_maturity has a high cardinality: 1939 distinct valuesHigh cardinality
offering_date has a high cardinality: 12765 distinct valuesHigh cardinality
delivery_date has a high cardinality: 11733 distinct valuesHigh cardinality
denomination has a high cardinality: 163 distinct valuesHigh cardinality
defeased_date has a high cardinality: 51 distinct valuesHigh cardinality
refunding_date has a high cardinality: 681 distinct valuesHigh cardinality
isin has a high cardinality: 559035 distinct valuesHigh cardinality
complete_cusip has a high cardinality: 561184 distinct valuesHigh cardinality
effective_date has a high cardinality: 11236 distinct valuesHigh cardinality
rating_decline_provision has a high cardinality: 257 distinct valuesHigh cardinality
determination_date has a high cardinality: 7739 distinct valuesHigh cardinality
reset_date has a high cardinality: 8964 distinct valuesHigh cardinality
determination_date_orig has a high cardinality: 8093 distinct valuesHigh cardinality
reset_date_orig has a high cardinality: 8127 distinct valuesHigh cardinality
conv_commod_issuer has a high cardinality: 3644 distinct valuesHigh cardinality
ticker has a high cardinality: 3263 distinct valuesHigh cardinality
conv_eff_date has a high cardinality: 6884 distinct valuesHigh cardinality
conv_exp_date has a high cardinality: 5826 distinct valuesHigh cardinality
conv_redemp_date has a high cardinality: 984 distinct valuesHigh cardinality
as_of_date has a high cardinality: 5892 distinct valuesHigh cardinality
change_date has a high cardinality: 6044 distinct valuesHigh cardinality
split_date has a high cardinality: 310 distinct valuesHigh cardinality
conv_prohibited_from has a high cardinality: 1140 distinct valuesHigh cardinality
coco_start_date has a high cardinality: 1365 distinct valuesHigh cardinality
coco_end_date has a high cardinality: 819 distinct valuesHigh cardinality
dated_date has a high cardinality: 10783 distinct valuesHigh cardinality
first_interest_date has a high cardinality: 12379 distinct valuesHigh cardinality
pay_in_kind_exp_date has a high cardinality: 244 distinct valuesHigh cardinality
last_interest_date has a high cardinality: 15403 distinct valuesHigh cardinality
filing_date has a high cardinality: 460 distinct valuesHigh cardinality
settlement has a high cardinality: 541 distinct valuesHigh cardinality
overallotment_expiration_date has a high cardinality: 3023 distinct valuesHigh cardinality
exercised_date has a high cardinality: 2388 distinct valuesHigh cardinality
notification_period has a high cardinality: 462 distinct valuesHigh cardinality
next_put_date has a high cardinality: 153 distinct valuesHigh cardinality
security_level is highly imbalanced (66.1%)Imbalance
convertible is highly imbalanced (91.3%)Imbalance
asset_backed is highly imbalanced (98.0%)Imbalance
canadian is highly imbalanced (74.3%)Imbalance
oid is highly imbalanced (84.6%)Imbalance
foreign_currency is highly imbalanced (90.6%)Imbalance
slob is highly imbalanced (99.9%)Imbalance
issue_offered_global is highly imbalanced (69.1%)Imbalance
settlement_type is highly imbalanced (99.8%)Imbalance
comp_neg_exch_deal is highly imbalanced (89.7%)Imbalance
sec_reg_type1 is highly imbalanced (73.7%)Imbalance
sec_reg_type2 is highly imbalanced (77.4%)Imbalance
rule_144a is highly imbalanced (69.1%)Imbalance
unit_deal is highly imbalanced (99.1%)Imbalance
form_of_own is highly imbalanced (95.8%)Imbalance
denomination is highly imbalanced (74.5%)Imbalance
covenants is highly imbalanced (59.2%)Imbalance
defeased is highly imbalanced (99.8%)Imbalance
defeasance_type is highly imbalanced (68.2%)Imbalance
defaulted is highly imbalanced (98.3%)Imbalance
tender_exch_offer is highly imbalanced (83.2%)Imbalance
refund_protection is highly imbalanced (94.8%)Imbalance
putable is highly imbalanced (95.3%)Imbalance
overallotment_opt is highly imbalanced (93.3%)Imbalance
announced_call is highly imbalanced (99.8%)Imbalance
dep_eligibility is highly imbalanced (64.0%)Imbalance
private_placement is highly imbalanced (98.3%)Imbalance
press_release is highly imbalanced (86.2%)Imbalance
perpetual is highly imbalanced (96.1%)Imbalance
exchangeable is highly imbalanced (54.2%)Imbalance
fungible is highly imbalanced (65.5%)Imbalance
preferred_security is highly imbalanced (50.6%)Imbalance
action_type is highly imbalanced (60.0%)Imbalance
rating_decline_trigger_put is highly imbalanced (90.5%)Imbalance
declining_net_worth is highly imbalanced (93.6%)Imbalance
after_acquired_property_clause is highly imbalanced (87.7%)Imbalance
economic_cov_def is highly imbalanced (61.0%)Imbalance
greater_of is highly imbalanced (99.6%)Imbalance
lesser_of is highly imbalanced (99.9%)Imbalance
conv_commod_type is highly imbalanced (84.5%)Imbalance
exchange is highly imbalanced (75.8%)Imbalance
dilution_protection is highly imbalanced (98.6%)Imbalance
conv_redemp_exception is highly imbalanced (79.6%)Imbalance
conv_period_spec is highly imbalanced (78.7%)Imbalance
reason is highly imbalanced (82.1%)Imbalance
conditional_conv_terms is highly imbalanced (70.6%)Imbalance
soft_call_make_whole is highly imbalanced (98.1%)Imbalance
peps is highly imbalanced (95.8%)Imbalance
percs is highly imbalanced (99.8%)Imbalance
convert_on_call is highly imbalanced (85.2%)Imbalance
coco_trigger_expressed_as is highly imbalanced (85.1%)Imbalance
coco_change_frequency is highly imbalanced (68.1%)Imbalance
coco_trade_days_in_previous is highly imbalanced (84.2%)Imbalance
pay_in_kind is highly imbalanced (98.6%)Imbalance
coupon_change_indicator is highly imbalanced (68.3%)Imbalance
day_count_basis is highly imbalanced (76.0%)Imbalance
currency is highly imbalanced (51.7%)Imbalance
dividends_related_payments_is is highly imbalanced (53.6%)Imbalance
funded_debt_is is highly imbalanced (93.3%)Imbalance
investments is highly imbalanced (86.1%)Imbalance
liens_is is highly imbalanced (60.3%)Imbalance
maintenance_net_worth is highly imbalanced (88.0%)Imbalance
senior_debt_issuance is highly imbalanced (94.9%)Imbalance
stock_issuance_issuer is highly imbalanced (84.6%)Imbalance
stock_transfer_sale_disp is highly imbalanced (77.2%)Imbalance
subordinated_debt_issuance is highly imbalanced (81.7%)Imbalance
net_earnings_test_issuance is highly imbalanced (87.6%)Imbalance
fixed_charge_coverage_is is highly imbalanced (56.3%)Imbalance
leverage_test_is is highly imbalanced (99.1%)Imbalance
borrowing_restricted is highly imbalanced (98.5%)Imbalance
funded_debt_sub is highly imbalanced (92.9%)Imbalance
stock_issuance is highly imbalanced (63.7%)Imbalance
preferred_stock_issuance is highly imbalanced (59.9%)Imbalance
investments_unrestricted_subs is highly imbalanced (89.9%)Imbalance
sale_xfer_assets_unrestricted is highly imbalanced (97.1%)Imbalance
subsidiary_redesignation is highly imbalanced (71.7%)Imbalance
subsidiary_guarantee is highly imbalanced (51.4%)Imbalance
liens_sub is highly imbalanced (64.6%)Imbalance
fixed_charge_coverage_sub is highly imbalanced (56.8%)Imbalance
leverage_test_sub is highly imbalanced (99.4%)Imbalance
security_pledge has 561008 (> 99.9%) missing valuesMissing
gross_spread has 348655 (62.1%) missing valuesMissing
selling_concession has 413059 (73.6%) missing valuesMissing
reallowance has 491675 (87.6%) missing valuesMissing
sec_reg_type1 has 17619 (3.1%) missing valuesMissing
sec_reg_type2 has 560978 (> 99.9%) missing valuesMissing
treasury_spread has 498043 (88.7%) missing valuesMissing
treasury_maturity has 532762 (94.9%) missing valuesMissing
offering_date has 12863 (2.3%) missing valuesMissing
offering_price has 34135 (6.1%) missing valuesMissing
offering_yield has 278562 (49.6%) missing valuesMissing
delivery_date has 18179 (3.2%) missing valuesMissing
defeasance_type has 561132 (> 99.9%) missing valuesMissing
defeased_date has 561109 (> 99.9%) missing valuesMissing
refunding_date has 557859 (99.4%) missing valuesMissing
dep_eligibility has 36467 (6.5%) missing valuesMissing
subsequent_data has 119435 (21.3%) missing valuesMissing
press_release has 559790 (99.8%) missing valuesMissing
sedol has 561184 (100.0%) missing valuesMissing
registration_rights has 556380 (99.1%) missing valuesMissing
action_price has 359281 (64.0%) missing valuesMissing
action_amount has 334198 (59.6%) missing valuesMissing
negative_pledge_covenant has 515429 (91.8%) missing valuesMissing
covenant_defeas_wo_tax_conseq has 515429 (91.8%) missing valuesMissing
legal_defeasance has 515429 (91.8%) missing valuesMissing
defeasance_wo_tax_conseq has 515429 (91.8%) missing valuesMissing
cross_default has 515429 (91.8%) missing valuesMissing
cross_acceleration has 515429 (91.8%) missing valuesMissing
change_control_put_provisions has 515429 (91.8%) missing valuesMissing
voting_power_percentage has 538879 (96.0%) missing valuesMissing
voting_power_percentage_erp has 554906 (98.9%) missing valuesMissing
rating_decline_trigger_put has 515429 (91.8%) missing valuesMissing
rating_decline_provision has 560641 (99.9%) missing valuesMissing
declining_net_worth has 515429 (91.8%) missing valuesMissing
declining_net_worth_trigger has 560679 (99.9%) missing valuesMissing
declining_net_worth_percentage has 555327 (99.0%) missing valuesMissing
declining_net_worth_provisions has 561106 (> 99.9%) missing valuesMissing
after_acquired_property_clause has 515429 (91.8%) missing valuesMissing
economic_cov_def has 515429 (91.8%) missing valuesMissing
asset_sale_clause has 515429 (91.8%) missing valuesMissing
fix_frequency has 520934 (92.8%) missing valuesMissing
determination_date has 521183 (92.9%) missing valuesMissing
greater_of has 445449 (79.4%) missing valuesMissing
lesser_of has 445449 (79.4%) missing valuesMissing
see_note has 445447 (79.4%) missing valuesMissing
reset_date has 521267 (92.9%) missing valuesMissing
determination_date_orig has 520932 (92.8%) missing valuesMissing
reset_date_orig has 521026 (92.8%) missing valuesMissing
conv_commod_cusip has 555538 (99.0%) missing valuesMissing
conv_commod_issuer has 501489 (89.4%) missing valuesMissing
conv_commod_type has 501992 (89.5%) missing valuesMissing
exchange has 502251 (89.5%) missing valuesMissing
ticker has 502372 (89.5%) missing valuesMissing
conv_price has 554886 (98.9%) missing valuesMissing
qty_of_commod has 554860 (98.9%) missing valuesMissing
percent_of_outstanding_commod has 555668 (99.0%) missing valuesMissing
conv_cash has 559908 (99.8%) missing valuesMissing
conv_eff_date has 501500 (89.4%) missing valuesMissing
conv_exp_date has 501705 (89.4%) missing valuesMissing
dilution_protection has 501639 (89.4%) missing valuesMissing
commod_price has 502072 (89.5%) missing valuesMissing
conv_premium has 554976 (98.9%) missing valuesMissing
conv_redemp_exception has 521805 (93.0%) missing valuesMissing
conv_redemp_date has 559584 (99.7%) missing valuesMissing
conv_price_percent has 559140 (99.6%) missing valuesMissing
conv_part_trade_days has 559153 (99.6%) missing valuesMissing
conv_total_trade_days has 559153 (99.6%) missing valuesMissing
conv_period_spec has 521828 (93.0%) missing valuesMissing
conv_period_days has 559819 (99.8%) missing valuesMissing
agent_id has 501853 (89.4%) missing valuesMissing
shares_outstanding has 556029 (99.1%) missing valuesMissing
orig_conv_price has 554832 (98.9%) missing valuesMissing
orig_commod_price has 502065 (89.5%) missing valuesMissing
orig_conv_premium has 554964 (98.9%) missing valuesMissing
orig_shares_outstanding has 556456 (99.2%) missing valuesMissing
orig_percent_outstanding_com has 555766 (99.0%) missing valuesMissing
orig_qty_of_commod has 554885 (98.9%) missing valuesMissing
as_of_date has 501452 (89.4%) missing valuesMissing
reason has 502854 (89.6%) missing valuesMissing
change_date has 501452 (89.4%) missing valuesMissing
split_date has 560530 (99.9%) missing valuesMissing
split_ratio has 560530 (99.9%) missing valuesMissing
conditional_conv_terms has 522172 (93.0%) missing valuesMissing
soft_call_make_whole has 523231 (93.2%) missing valuesMissing
peps has 523207 (93.2%) missing valuesMissing
percs has 523299 (93.2%) missing valuesMissing
conv_prohibited_from has 559523 (99.7%) missing valuesMissing
convert_on_call has 522837 (93.2%) missing valuesMissing
coco_start_date has 559034 (99.6%) missing valuesMissing
coco_end_date has 559063 (99.6%) missing valuesMissing
coco_initial_trigger_percent has 559137 (99.6%) missing valuesMissing
coco_trigger_expressed_as has 559142 (99.6%) missing valuesMissing
coco_change_rate has 559660 (99.7%) missing valuesMissing
coco_min_trigger_level has 559366 (99.7%) missing valuesMissing
coco_change_frequency has 559156 (99.6%) missing valuesMissing
coco_trade_days has 559164 (99.6%) missing valuesMissing
coco_trade_days_in_previous has 559161 (99.6%) missing valuesMissing
sc_make_whole_start_date has 561139 (> 99.9%) missing valuesMissing
sc_make_whole_end_date has 561139 (> 99.9%) missing valuesMissing
sc_make_whole_decrement_type has 561142 (> 99.9%) missing valuesMissing
sc_make_whole_initial_amount has 561163 (> 99.9%) missing valuesMissing
sc_make_whole_change_percent has 561163 (> 99.9%) missing valuesMissing
peps_max_conversion_ratio has 561010 (> 99.9%) missing valuesMissing
peps_min_conversion_ratio has 561010 (> 99.9%) missing valuesMissing
peps_higher_price has 561010 (> 99.9%) missing valuesMissing
peps_lower_price has 561010 (> 99.9%) missing valuesMissing
peps_issue_price has 561022 (> 99.9%) missing valuesMissing
percs_max_payoff has 561181 (> 99.9%) missing valuesMissing
dated_date has 17777 (3.2%) missing valuesMissing
first_interest_date has 114332 (20.4%) missing valuesMissing
coupon has 80086 (14.3%) missing valuesMissing
pay_in_kind has 118847 (21.2%) missing valuesMissing
pay_in_kind_exp_date has 560853 (99.9%) missing valuesMissing
last_interest_date has 163278 (29.1%) missing valuesMissing
next_interest_date has 561184 (100.0%) missing valuesMissing
currency has 554471 (98.8%) missing valuesMissing
amt_offered has 554471 (98.8%) missing valuesMissing
conversion_rate has 561136 (> 99.9%) missing valuesMissing
consolidation_merger has 515432 (91.8%) missing valuesMissing
dividends_related_payments_is has 515432 (91.8%) missing valuesMissing
funded_debt_is has 515432 (91.8%) missing valuesMissing
indebtedness_is has 515432 (91.8%) missing valuesMissing
investments has 515432 (91.8%) missing valuesMissing
liens_is has 515432 (91.8%) missing valuesMissing
maintenance_net_worth has 515432 (91.8%) missing valuesMissing
restricted_payments has 515432 (91.8%) missing valuesMissing
sales_leaseback_is has 515432 (91.8%) missing valuesMissing
sale_assets has 515432 (91.8%) missing valuesMissing
senior_debt_issuance has 515432 (91.8%) missing valuesMissing
stock_issuance_issuer has 515432 (91.8%) missing valuesMissing
stock_transfer_sale_disp has 515432 (91.8%) missing valuesMissing
subordinated_debt_issuance has 515432 (91.8%) missing valuesMissing
transaction_affiliates has 515432 (91.8%) missing valuesMissing
net_earnings_test_issuance has 515432 (91.8%) missing valuesMissing
fixed_charge_coverage_is has 515432 (91.8%) missing valuesMissing
leverage_test_is has 515432 (91.8%) missing valuesMissing
issuer_id_affected has 560023 (99.8%) missing valuesMissing
filing_date has 560023 (99.8%) missing valuesMissing
settlement has 560077 (99.8%) missing valuesMissing
other_sec_type has 561154 (> 99.9%) missing valuesMissing
other_sec_issuer has 561107 (> 99.9%) missing valuesMissing
sec_cusip has 561173 (> 99.9%) missing valuesMissing
quantity has 561106 (> 99.9%) missing valuesMissing
date_transferable has 561154 (> 99.9%) missing valuesMissing
date_subj_adjustment has 561154 (> 99.9%) missing valuesMissing
market_price has 561125 (> 99.9%) missing valuesMissing
allocated_offering_price_other has 561118 (> 99.9%) missing valuesMissing
overallotment_expiration_date has 556812 (99.2%) missing valuesMissing
exercised has 557173 (99.3%) missing valuesMissing
exercised_date has 558015 (99.4%) missing valuesMissing
amount has 556902 (99.2%) missing valuesMissing
notification_period has 559100 (99.6%) missing valuesMissing
next_put_date has 560943 (> 99.9%) missing valuesMissing
next_put_price has 560941 (> 99.9%) missing valuesMissing
borrowing_restricted has 515432 (91.8%) missing valuesMissing
dividends_related_payments_sub has 515432 (91.8%) missing valuesMissing
funded_debt_sub has 515432 (91.8%) missing valuesMissing
indebtedness_sub has 515432 (91.8%) missing valuesMissing
stock_issuance has 515432 (91.8%) missing valuesMissing
preferred_stock_issuance has 515432 (91.8%) missing valuesMissing
investments_unrestricted_subs has 515432 (91.8%) missing valuesMissing
sale_xfer_assets_unrestricted has 515432 (91.8%) missing valuesMissing
subsidiary_redesignation has 515432 (91.8%) missing valuesMissing
subsidiary_guarantee has 515432 (91.8%) missing valuesMissing
sales_leaseback_sub has 515432 (91.8%) missing valuesMissing
liens_sub has 515432 (91.8%) missing valuesMissing
fixed_charge_coverage_sub has 515432 (91.8%) missing valuesMissing
leverage_test_sub has 515432 (91.8%) missing valuesMissing
unit_cusip has 560998 (> 99.9%) missing valuesMissing
total_units_offered has 561016 (> 99.9%) missing valuesMissing
principal_amt_per_unit has 561029 (> 99.9%) missing valuesMissing
allocated_offering_price_unit has 560968 (> 99.9%) missing valuesMissing
gross_spread is highly skewed (γ1 = 326.5143639)Skewed
selling_concession is highly skewed (γ1 = 283.0784248)Skewed
reallowance is highly skewed (γ1 = 263.6455954)Skewed
offering_amt is highly skewed (γ1 = 164.8115636)Skewed
offering_price is highly skewed (γ1 = 302.3042203)Skewed
offering_yield is highly skewed (γ1 = 531.5041634)Skewed
principal_amt is highly skewed (γ1 = 282.5217547)Skewed
action_price is highly skewed (γ1 = 388.8830975)Skewed
action_amount is highly skewed (γ1 = 229.9638808)Skewed
amount_outstanding is highly skewed (γ1 = 349.2550159)Skewed
voting_power_percentage_erp is highly skewed (γ1 = 65.35959217)Skewed
conv_price is highly skewed (γ1 = 47.14630946)Skewed
percent_of_outstanding_commod is highly skewed (γ1 = 70.32664758)Skewed
conv_cash is highly skewed (γ1 = 24.65621726)Skewed
commod_price is highly skewed (γ1 = 120.8609144)Skewed
conv_premium is highly skewed (γ1 = 50.47190035)Skewed
orig_conv_price is highly skewed (γ1 = 49.64510861)Skewed
orig_commod_price is highly skewed (γ1 = 119.4230657)Skewed
orig_conv_premium is highly skewed (γ1 = 75.92180339)Skewed
orig_percent_outstanding_com is highly skewed (γ1 = 73.16424642)Skewed
orig_qty_of_commod is highly skewed (γ1 = 53.26868872)Skewed
amt_offered is highly skewed (γ1 = 48.4082822)Skewed
amount is highly skewed (γ1 = 47.88437067)Skewed
issue_id has unique valuesUnique
complete_cusip has unique valuesUnique
sedol is an unsupported type, check if it needs cleaning or further analysisUnsupported
conv_commod_cusip is an unsupported type, check if it needs cleaning or further analysisUnsupported
next_interest_date is an unsupported type, check if it needs cleaning or further analysisUnsupported
sec_cusip is an unsupported type, check if it needs cleaning or further analysisUnsupported
unit_cusip is an unsupported type, check if it needs cleaning or further analysisUnsupported
gross_spread has 25392 (4.5%) zerosZeros
selling_concession has 22388 (4.0%) zerosZeros
reallowance has 18391 (3.3%) zerosZeros
treasury_spread has 16128 (2.9%) zerosZeros
amount_outstanding has 456041 (81.3%) zerosZeros
voting_power_percentage_erp has 6256 (1.1%) zerosZeros
declining_net_worth_percentage has 5639 (1.0%) zerosZeros
interest_frequency has 105541 (18.8%) zerosZeros
coupon has 111965 (20.0%) zerosZeros

Reproduction

Analysis started2023-03-30 16:44:44.761625
Analysis finished2023-03-30 16:45:16.305537
Duration31.54 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

Distinct500000
Distinct (%)89.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean226078.1853
Minimum0
Maximum499999
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:16.524629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14029.15
Q179111.75
median219407.5
Q3359703.25
95-th percentile471939.85
Maximum499999
Range499999
Interquartile range (IQR)280591.5

Descriptive statistics

Standard deviation152552.6034
Coefficient of variation (CV)0.6747780783
Kurtosis-1.288151716
Mean226078.1853
Median Absolute Deviation (MAD)140296
Skewness0.1436078777
Sum1.268714603 × 1011
Variance2.327229681 × 1010
MonotonicityNot monotonic
2023-03-30T18:45:16.664802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2
 
< 0.1%
40795 2
 
< 0.1%
40783 2
 
< 0.1%
40784 2
 
< 0.1%
40785 2
 
< 0.1%
40786 2
 
< 0.1%
40787 2
 
< 0.1%
40788 2
 
< 0.1%
40789 2
 
< 0.1%
40790 2
 
< 0.1%
Other values (499990) 561164
> 99.9%
ValueCountFrequency (%)
0 2
< 0.1%
1 2
< 0.1%
2 2
< 0.1%
3 2
< 0.1%
4 2
< 0.1%
ValueCountFrequency (%)
499999 1
< 0.1%
499998 1
< 0.1%
499997 1
< 0.1%
499996 1
< 0.1%
499995 1
< 0.1%

issue_id
Real number (ℝ)

Distinct561184
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean509570.505
Minimum1
Maximum1042557
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:16.793579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile33794.15
Q1166955.75
median553395
Q3754332.5
95-th percentile1001197.85
Maximum1042557
Range1042556
Interquartile range (IQR)587376.75

Descriptive statistics

Standard deviation321824.275
Coefficient of variation (CV)0.6315598565
Kurtosis-1.299860495
Mean509570.505
Median Absolute Deviation (MAD)283737
Skewness-0.05286287657
Sum2.859628143 × 1011
Variance1.03570864 × 1011
MonotonicityNot monotonic
2023-03-30T18:45:16.917698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
697086 1
 
< 0.1%
697074 1
 
< 0.1%
697076 1
 
< 0.1%
697078 1
 
< 0.1%
697080 1
 
< 0.1%
697082 1
 
< 0.1%
697084 1
 
< 0.1%
697088 1
 
< 0.1%
697036 1
 
< 0.1%
Other values (561174) 561174
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
ValueCountFrequency (%)
1042557 1
< 0.1%
1042556 1
< 0.1%
1042555 1
< 0.1%
1042554 1
< 0.1%
1042553 1
< 0.1%

issuer_id
Real number (ℝ)

Distinct16548
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18680.76198
Minimum3
Maximum51760
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:17.037618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile1484
Q11489
median5099
Q337581
95-th percentile46419
Maximum51760
Range51757
Interquartile range (IQR)36092

Descriptive statistics

Standard deviation18509.9584
Coefficient of variation (CV)0.9908567126
Kurtosis-1.74687919
Mean18680.76198
Median Absolute Deviation (MAD)3844
Skewness0.2986907322
Sum1.048334473 × 1010
Variance342618560.1
MonotonicityNot monotonic
2023-03-30T18:45:17.159697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1489 105092
18.7%
1494 35797
 
6.4%
41105 35106
 
6.3%
1497 29948
 
5.3%
1484 21783
 
3.9%
29777 21432
 
3.8%
46451 18485
 
3.3%
40163 13968
 
2.5%
37118 12053
 
2.1%
8276 11539
 
2.1%
Other values (16538) 255981
45.6%
ValueCountFrequency (%)
3 15
 
< 0.1%
4 1887
0.3%
6 5
 
< 0.1%
7 3
 
< 0.1%
8 4
 
< 0.1%
ValueCountFrequency (%)
51760 1
< 0.1%
51702 1
< 0.1%
51689 1
< 0.1%
51685 1
< 0.1%
51684 1
< 0.1%
Distinct18920
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
FEDERAL HOME LN BKS
103315 
UBS AG LONDON BRH
35101 
FEDERAL HOME LN MTG CORP MTN
 
29588
FEDERAL NATL MTG ASSN
 
23498
BARCLAYS BK PLC
 
21433
Other values (18915)
348249 

Unique

Unique8653 ?
Unique (%)1.5%

Sample

1st rowAAR CORP
2nd rowAAR CORP
3rd rowABN AMRO BK N V N Y BRH
4th rowABN AMRO BK N V N Y BRH
5th rowABN AMRO BK N V N Y BRH

Common Values

ValueCountFrequency (%)
FEDERAL HOME LN BKS 103315
 
18.4%
UBS AG LONDON BRH 35101
 
6.3%
FEDERAL HOME LN MTG CORP MTN 29588
 
5.3%
FEDERAL NATL MTG ASSN 23498
 
4.2%
BARCLAYS BK PLC 21433
 
3.8%
FEDERAL FARM CR BKS CONS SYSTEMWIDE BDS 19120
 
3.4%
JPMORGAN CHASE FINL CO LLC 18484
 
3.3%
GS FIN CORP 13966
 
2.5%
CITIGROUP GLOBAL MKTS HLDGS INC 12026
 
2.1%
ROYAL BK CDA 11538
 
2.1%
Other values (18910) 273115
48.7%

issuer_cusip
Categorical

Distinct20621
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
36962G
 
944
312923
 
908
3133M1
 
906
313394
 
906
38143U
 
905
Other values (20616)
556615 

Unique

Unique7964 ?
Unique (%)1.4%

Sample

1st row000361
2nd row000361
3rd row00077D
4th row00077D
5th row00077T

Common Values

ValueCountFrequency (%)
36962G 944
 
0.2%
312923 908
 
0.2%
3133M1 906
 
0.2%
313394 906
 
0.2%
38143U 905
 
0.2%
312925 904
 
0.2%
313395 904
 
0.2%
313393 903
 
0.2%
3133M5 902
 
0.2%
3133M0 902
 
0.2%
Other values (20611) 552100
98.4%

issue_cusip
Categorical

Distinct10315
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
AA8
 
1330
AA1
 
1316
AA9
 
1307
AA7
 
1297
AA5
 
1295
Other values (10310)
554639 

Unique

Unique298 ?
Unique (%)0.1%

Sample

1st rowAA3
2nd rowAB1
3rd rowAB5
4th rowAF6
5th rowAA2

Common Values

ValueCountFrequency (%)
AA8 1330
 
0.2%
AA1 1316
 
0.2%
AA9 1307
 
0.2%
AA7 1297
 
0.2%
AA5 1295
 
0.2%
AA3 1293
 
0.2%
AA4 1285
 
0.2%
AA6 1277
 
0.2%
AA0 1265
 
0.2%
AA2 1247
 
0.2%
Other values (10305) 548272
97.7%

issue_name
Categorical

Distinct136216
Distinct (%)24.3%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
NT
 
28350
MTN
 
27455
BD
 
17206
MTN FLTG RT EQUITY-LINKED (TRIGGER PHOENIX AUTOCALLABLE OPT)
 
10647
DEB
 
6418
Other values (136211)
471108 

Unique

Unique125755 ?
Unique (%)22.4%

Sample

1st rowNT
2nd rowNT
3rd rowMTN
4th rowSUB DEP NT SER B
5th rowSUB DEP NT SER B

Common Values

ValueCountFrequency (%)
NT 28350
 
5.1%
MTN 27455
 
4.9%
BD 17206
 
3.1%
MTN FLTG RT EQUITY-LINKED (TRIGGER PHOENIX AUTOCALLABLE OPT) 10647
 
1.9%
DEB 6418
 
1.1%
SR NT RULE 144A 6357
 
1.1%
SR NT 6055
 
1.1%
MTN FLTG RT RULE 144A 5477
 
1.0%
MTN STEP-UP 5410
 
1.0%
MTN FLTG RT SER B EQUITY-LINKED (TRIGGER PHOENIX AUTOCALL OPT) 4928
 
0.9%
Other values (136206) 442881
78.9%

maturity
Categorical

Distinct16750
Distinct (%)3.0%
Missing2290
Missing (%)0.4%
Memory size4.3 MiB
2021-09-30
 
427
2023-08-31
 
409
2024-05-31
 
391
2023-06-15
 
388
2021-06-30
 
381
Other values (16745)
556898 

Unique

Unique3110 ?
Unique (%)0.6%

Sample

1st row2001-11-01
2nd row2003-10-15
3rd row1996-01-12
4th row2009-08-01
5th row2023-05-15

Common Values

ValueCountFrequency (%)
2021-09-30 427
 
0.1%
2023-08-31 409
 
0.1%
2024-05-31 391
 
0.1%
2023-06-15 388
 
0.1%
2021-06-30 381
 
0.1%
2022-06-30 379
 
0.1%
2022-11-30 368
 
0.1%
2008-04-30 360
 
0.1%
2023-02-28 355
 
0.1%
2019-09-30 355
 
0.1%
Other values (16740) 555081
98.9%
(Missing) 2290
 
0.4%

security_level
Categorical

Distinct7
Distinct (%)< 0.1%
Missing348
Missing (%)0.1%
Memory size4.3 MiB
SEN
441971 
NON
96568 
SS
 
10213
SENS
 
9208
SUB
 
2524
Other values (2)
 
352

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSEN
2nd rowSEN
3rd rowSEN
4th rowSENS
5th rowSUB

Common Values

ValueCountFrequency (%)
SEN 441971
78.8%
NON 96568
 
17.2%
SS 10213
 
1.8%
SENS 9208
 
1.6%
SUB 2524
 
0.4%
JUNS 337
 
0.1%
JUN 15
 
< 0.1%
(Missing) 348
 
0.1%

Common Values (Plot)

2023-03-30T18:45:17.372753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

security_pledge
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.6%
Missing561008
Missing (%)> 99.9%
Memory size4.3 MiB
M
176 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
M 176
 
< 0.1%
(Missing) 561008
> 99.9%

Common Values (Plot)

2023-03-30T18:45:17.477103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

enhancement
Categorical

Distinct2
Distinct (%)< 0.1%
Missing724
Missing (%)0.1%
Memory size4.3 MiB
N
478295 
Y
82165 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 478295
85.2%
Y 82165
 
14.6%
(Missing) 724
 
0.1%

Common Values (Plot)

2023-03-30T18:45:17.554697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

coupon_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing31
Missing (%)< 0.1%
Memory size4.3 MiB
F
296824 
V
158415 
Z
105914 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowF
4th rowF
5th rowF

Common Values

ValueCountFrequency (%)
F 296824
52.9%
V 158415
28.2%
Z 105914
 
18.9%
(Missing) 31
 
< 0.1%

Common Values (Plot)

2023-03-30T18:45:17.639518image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

convertible
Categorical

Distinct2
Distinct (%)< 0.1%
Missing925
Missing (%)0.2%
Memory size4.3 MiB
N
554156 
Y
 
6103

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 554156
98.7%
Y 6103
 
1.1%
(Missing) 925
 
0.2%

Common Values (Plot)

2023-03-30T18:45:17.726675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

mtn
Categorical

Distinct2
Distinct (%)< 0.1%
Missing34
Missing (%)< 0.1%
Memory size4.3 MiB
Y
312774 
N
248376 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowY
4th rowY
5th rowN

Common Values

ValueCountFrequency (%)
Y 312774
55.7%
N 248376
44.3%
(Missing) 34
 
< 0.1%

Common Values (Plot)

2023-03-30T18:45:17.804348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

asset_backed
Categorical

Distinct2
Distinct (%)< 0.1%
Missing928
Missing (%)0.2%
Memory size4.3 MiB
N
559166 
Y
 
1090

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 559166
99.6%
Y 1090
 
0.2%
(Missing) 928
 
0.2%

Common Values (Plot)

2023-03-30T18:45:18.274433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

yankee
Categorical

Distinct2
Distinct (%)< 0.1%
Missing717
Missing (%)0.1%
Memory size4.3 MiB
N
463363 
Y
97104 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 463363
82.6%
Y 97104
 
17.3%
(Missing) 717
 
0.1%

Common Values (Plot)

2023-03-30T18:45:18.356365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

canadian
Categorical

Distinct2
Distinct (%)< 0.1%
Missing882
Missing (%)0.2%
Memory size4.3 MiB
N
536067 
Y
 
24235

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 536067
95.5%
Y 24235
 
4.3%
(Missing) 882
 
0.2%

Common Values (Plot)

2023-03-30T18:45:18.445142image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

oid
Categorical

Distinct2
Distinct (%)< 0.1%
Missing924
Missing (%)0.2%
Memory size4.3 MiB
N
547804 
Y
 
12456

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 547804
97.6%
Y 12456
 
2.2%
(Missing) 924
 
0.2%

Common Values (Plot)

2023-03-30T18:45:18.538463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

foreign_currency
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
N
554470 
Y
 
6714

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 554470
98.8%
Y 6714
 
1.2%

Common Values (Plot)

2023-03-30T18:45:18.627302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

slob
Categorical

Distinct2
Distinct (%)< 0.1%
Missing928
Missing (%)0.2%
Memory size4.3 MiB
N
560225 
Y
 
31

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 560225
99.8%
Y 31
 
< 0.1%
(Missing) 928
 
0.2%

Common Values (Plot)

2023-03-30T18:45:18.720459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing813
Missing (%)0.1%
Memory size4.3 MiB
N
529349 
Y
 
31022

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 529349
94.3%
Y 31022
 
5.5%
(Missing) 813
 
0.1%

Common Values (Plot)

2023-03-30T18:45:18.811389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

settlement_type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing93
Missing (%)< 0.1%
Memory size4.3 MiB
S
560996 
N
 
95

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowS
3rd rowS
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
S 560996
> 99.9%
N 95
 
< 0.1%
(Missing) 93
 
< 0.1%

Common Values (Plot)

2023-03-30T18:45:18.900024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

gross_spread
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct9285
Distinct (%)4.4%
Missing348655
Missing (%)62.1%
Infinite0
Infinite (%)0.0%
Mean4.763115679
Minimum0
Maximum45000
Zeros25392
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:19.012862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.75
median1.8
Q34.5
95-th percentile20
Maximum45000
Range45000
Interquartile range (IQR)3.75

Descriptive statistics

Standard deviation121.8781552
Coefficient of variation (CV)25.58790578
Kurtosis111203.0936
Mean4.763115679
Median Absolute Deviation (MAD)1.33
Skewness326.5143639
Sum1012300.212
Variance14854.28472
MonotonicityNot monotonic
2023-03-30T18:45:19.147907image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25392
 
4.5%
2 13846
 
2.5%
1.5 13413
 
2.4%
1.25 10568
 
1.9%
3 10191
 
1.8%
2.5 7016
 
1.3%
1.75 5482
 
1.0%
1 5369
 
1.0%
6.5 5284
 
0.9%
8.75 4443
 
0.8%
Other values (9275) 111525
 
19.9%
(Missing) 348655
62.1%
ValueCountFrequency (%)
0 25392
4.5%
0.001 9
 
< 0.1%
0.0017 1
 
< 0.1%
0.002 18
 
< 0.1%
0.0025 1
 
< 0.1%
ValueCountFrequency (%)
45000 1
< 0.1%
32500 1
< 0.1%
5043 1
< 0.1%
3000 2
< 0.1%
2500 1
< 0.1%

selling_concession
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct1054
Distinct (%)0.7%
Missing413059
Missing (%)73.6%
Infinite0
Infinite (%)0.0%
Mean2.905805492
Minimum0
Maximum30000
Zeros22388
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:19.287348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.75
median1.3
Q32.5
95-th percentile10.625
Maximum30000
Range30000
Interquartile range (IQR)1.75

Descriptive statistics

Standard deviation94.16006429
Coefficient of variation (CV)32.40411808
Kurtosis83274.14834
Mean2.905805492
Median Absolute Deviation (MAD)0.95
Skewness283.0784248
Sum430422.4385
Variance8866.117707
MonotonicityNot monotonic
2023-03-30T18:45:19.431025image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 22388
 
4.0%
1.25 17934
 
3.2%
1.5 17899
 
3.2%
1 14981
 
2.7%
2.5 10950
 
2.0%
2 7858
 
1.4%
4 6253
 
1.1%
5 5087
 
0.9%
0.75 4641
 
0.8%
0.5 4560
 
0.8%
Other values (1044) 35574
 
6.3%
(Missing) 413059
73.6%
ValueCountFrequency (%)
0 22388
4.0%
0.002 1
 
< 0.1%
0.005 8
 
< 0.1%
0.006 1
 
< 0.1%
0.01 30
 
< 0.1%
ValueCountFrequency (%)
30000 1
< 0.1%
20000 1
< 0.1%
1875 1
< 0.1%
1500 2
< 0.1%
1010.9375 1
< 0.1%

reallowance
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct303
Distinct (%)0.4%
Missing491675
Missing (%)87.6%
Infinite0
Infinite (%)0.0%
Mean2153.54541
Minimum0
Maximum149557500
Zeros18391
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:19.583588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.25
Q32.5
95-th percentile6.25
Maximum149557500
Range149557500
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation567267.1976
Coefficient of variation (CV)263.4108364
Kurtosis69508.99997
Mean2153.54541
Median Absolute Deviation (MAD)1.25
Skewness263.6455954
Sum149690787.9
Variance3.217920735 × 1011
MonotonicityNot monotonic
2023-03-30T18:45:19.728763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 18391
 
3.3%
2.5 15604
 
2.8%
1.25 5311
 
0.9%
1 4911
 
0.9%
2 3707
 
0.7%
1.5 3385
 
0.6%
0.75 2444
 
0.4%
3.5 1495
 
0.3%
2.25 1365
 
0.2%
0.5 1333
 
0.2%
Other values (293) 11563
 
2.1%
(Missing) 491675
87.6%
ValueCountFrequency (%)
0 18391
3.3%
0.005 1
 
< 0.1%
0.01 4
 
< 0.1%
0.01134 1
 
< 0.1%
0.025 3
 
< 0.1%
ValueCountFrequency (%)
149557500 1
 
< 0.1%
1250 1
 
< 0.1%
1011.07375 1
 
< 0.1%
1000 1
 
< 0.1%
250 4
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing8
Missing (%)< 0.1%
Memory size4.3 MiB
NEG
548465 
EXCH
 
12046
COMP
 
665

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNEG
2nd rowNEG
3rd rowNEG
4th rowNEG
5th rowNEG

Common Values

ValueCountFrequency (%)
NEG 548465
97.7%
EXCH 12046
 
2.1%
COMP 665
 
0.1%
(Missing) 8
 
< 0.1%

Common Values (Plot)

2023-03-30T18:45:19.862089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

rule_415_reg
Categorical

Distinct2
Distinct (%)< 0.1%
Missing794
Missing (%)0.1%
Memory size4.3 MiB
Y
281234 
N
279156 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowY
3rd rowY
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
Y 281234
50.1%
N 279156
49.7%
(Missing) 794
 
0.1%

Common Values (Plot)

2023-03-30T18:45:19.955659image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

sec_reg_type1
Categorical

IMBALANCE  MISSING 

Distinct27
Distinct (%)< 0.1%
Missing17619
Missing (%)3.1%
Memory size4.3 MiB
RBNA
288453 
NR
235741 
S-3
 
11439
S-4
 
4084
F-3
 
911
Other values (22)
 
2937

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowRBNA
2nd rowS-3
3rd rowRBNA
4th rowNR
5th rowNR

Common Values

ValueCountFrequency (%)
RBNA 288453
51.4%
NR 235741
42.0%
S-3 11439
 
2.0%
S-4 4084
 
0.7%
F-3 911
 
0.2%
RS 898
 
0.2%
S-1 794
 
0.1%
F-4 517
 
0.1%
S-2 163
 
< 0.1%
F-10 144
 
< 0.1%
Other values (17) 421
 
0.1%
(Missing) 17619
 
3.1%

sec_reg_type2
Categorical

IMBALANCE  MISSING 

Distinct6
Distinct (%)2.9%
Missing560978
Missing (%)> 99.9%
Memory size4.3 MiB
NR
186 
S-3
 
15
F-10
 
2
F-3
 
1
A
 
1

Unique

Unique3 ?
Unique (%)1.5%

Sample

1st rowNR
2nd rowNR
3rd rowNR
4th rowNR
5th rowNR

Common Values

ValueCountFrequency (%)
NR 186
 
< 0.1%
S-3 15
 
< 0.1%
F-10 2
 
< 0.1%
F-3 1
 
< 0.1%
A 1
 
< 0.1%
S-1 1
 
< 0.1%
(Missing) 560978
> 99.9%

Common Values (Plot)

2023-03-30T18:45:20.055588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

rule_144a
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
N
530140 
Y
 
31044

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 530140
94.5%
Y 31044
 
5.5%

Common Values (Plot)

2023-03-30T18:45:20.151183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

treasury_spread
Real number (ℝ)

MISSING  ZEROS 

Distinct2916
Distinct (%)4.6%
Missing498043
Missing (%)88.7%
Infinite0
Infinite (%)0.0%
Mean138.419537
Minimum-292
Maximum3916
Zeros16128
Zeros (%)2.9%
Negative68
Negative (%)< 0.1%
Memory size4.3 MiB
2023-03-30T18:45:20.242322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-292
5-th percentile0
Q10
median81
Q3175
95-th percentile540
Maximum3916
Range4208
Interquartile range (IQR)175

Descriptive statistics

Standard deviation181.0807358
Coefficient of variation (CV)1.308202149
Kurtosis8.337203624
Mean138.419537
Median Absolute Deviation (MAD)81
Skewness2.279389946
Sum8739947.986
Variance32790.23287
MonotonicityNot monotonic
2023-03-30T18:45:20.355441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16128
 
2.9%
100 617
 
0.1%
90 596
 
0.1%
80 572
 
0.1%
95 570
 
0.1%
120 560
 
0.1%
70 528
 
0.1%
105 525
 
0.1%
110 522
 
0.1%
115 517
 
0.1%
Other values (2906) 42006
 
7.5%
(Missing) 498043
88.7%
ValueCountFrequency (%)
-292 1
< 0.1%
-290 1
< 0.1%
-288 1
< 0.1%
-287 1
< 0.1%
-286.5 1
< 0.1%
ValueCountFrequency (%)
3916 1
< 0.1%
1654 1
< 0.1%
1568 1
< 0.1%
1532 1
< 0.1%
1501 1
< 0.1%

treasury_maturity
Categorical

HIGH CARDINALITY  MISSING 

Distinct1939
Distinct (%)6.8%
Missing532762
Missing (%)94.9%
Memory size4.3 MiB
10 YEAR
3472 
5 YEAR
2579 
3 YEAR
 
1578
30 YEAR
 
1214
7 YEAR
 
960
Other values (1934)
18619 

Unique

Unique855 ?
Unique (%)3.0%

Sample

1st row10 YEAR
2nd row2 YEAR
3rd row10 YEAR
4th row30 YEAR
5th row30 YEAR

Common Values

ValueCountFrequency (%)
10 YEAR 3472
 
0.6%
5 YEAR 2579
 
0.5%
3 YEAR 1578
 
0.3%
30 YEAR 1214
 
0.2%
7 YEAR 960
 
0.2%
2 YEAR 828
 
0.1%
1 YEAR 549
 
0.1%
4 YEAR 301
 
0.1%
912828Z94 209
 
< 0.1%
912828KQ2 173
 
< 0.1%
Other values (1929) 16559
 
3.0%
(Missing) 532762
94.9%

offering_amt
Real number (ℝ)

Distinct37250
Distinct (%)6.6%
Missing61
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean348951.3876
Minimum0
Maximum2000000000
Zeros83
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:20.478031image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile100
Q11810
median15000
Q375000
95-th percentile750000
Maximum2000000000
Range2000000000
Interquartile range (IQR)73190

Descriptive statistics

Standard deviation6665280.246
Coefficient of variation (CV)19.10088477
Kurtosis38241.93252
Mean348951.3876
Median Absolute Deviation (MAD)14520
Skewness164.8115636
Sum1.958046494 × 1011
Variance4.442596075 × 1013
MonotonicityNot monotonic
2023-03-30T18:45:20.759667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25000 37840
 
6.7%
50000 30316
 
5.4%
15000 26919
 
4.8%
100000 21412
 
3.8%
1 15920
 
2.8%
500000 11247
 
2.0%
10000 11054
 
2.0%
250000 10873
 
1.9%
20000 9389
 
1.7%
200000 8385
 
1.5%
Other values (37240) 377768
67.3%
ValueCountFrequency (%)
0 83
 
< 0.1%
0.04 1
 
< 0.1%
0.45 1
 
< 0.1%
1 15920
2.8%
1.06 1
 
< 0.1%
ValueCountFrequency (%)
2000000000 1
 
< 0.1%
1924515000 1
 
< 0.1%
1420000000 1
 
< 0.1%
1250000000 1
 
< 0.1%
1000000000 4
< 0.1%

offering_date
Categorical

HIGH CARDINALITY  MISSING 

Distinct12765
Distinct (%)2.3%
Missing12863
Missing (%)2.3%
Memory size4.3 MiB
2022-04-29
 
471
2021-03-26
 
450
2021-01-29
 
427
2020-11-24
 
413
2020-01-28
 
409
Other values (12760)
546151 

Unique

Unique2263 ?
Unique (%)0.4%

Sample

1st row1989-10-24
2nd row1993-10-12
3rd row1994-01-07
4th row1994-07-27
5th row1993-05-20

Common Values

ValueCountFrequency (%)
2022-04-29 471
 
0.1%
2021-03-26 450
 
0.1%
2021-01-29 427
 
0.1%
2020-11-24 413
 
0.1%
2020-01-28 409
 
0.1%
2020-02-28 408
 
0.1%
2021-02-26 400
 
0.1%
2020-02-25 397
 
0.1%
2021-09-27 396
 
0.1%
2022-05-31 393
 
0.1%
Other values (12755) 544157
97.0%
(Missing) 12863
 
2.3%

offering_price
Real number (ℝ)

MISSING  SKEWED 

Distinct8238
Distinct (%)1.6%
Missing34135
Missing (%)6.1%
Infinite0
Infinite (%)0.0%
Mean99.30603505
Minimum0
Maximum10010
Zeros2191
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:20.931193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile99.69564
Q1100
median100
Q3100
95-th percentile100
Maximum10010
Range10010
Interquartile range (IQR)0

Descriptive statistics

Standard deviation19.28913944
Coefficient of variation (CV)0.1942393474
Kurtosis141617.3136
Mean99.30603505
Median Absolute Deviation (MAD)0
Skewness302.3042203
Sum52339146.47
Variance372.0709005
MonotonicityNot monotonic
2023-03-30T18:45:21.062302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 475288
84.7%
0 2191
 
0.4%
99.5 631
 
0.1%
99.75 522
 
0.1%
99 367
 
0.1%
99.875 289
 
0.1%
99.25 276
 
< 0.1%
99.9 238
 
< 0.1%
99.8 226
 
< 0.1%
99.625 194
 
< 0.1%
Other values (8228) 46827
 
8.3%
(Missing) 34135
 
6.1%
ValueCountFrequency (%)
0 2191
0.4%
1 10
 
< 0.1%
1.38 1
 
< 0.1%
1.45 2
 
< 0.1%
1.68 1
 
< 0.1%
ValueCountFrequency (%)
10010 1
< 0.1%
5000 1
< 0.1%
3100 1
< 0.1%
1400 2
< 0.1%
1001 1
< 0.1%

offering_yield
Real number (ℝ)

MISSING  SKEWED 

Distinct33438
Distinct (%)11.8%
Missing278562
Missing (%)49.6%
Infinite0
Infinite (%)0.0%
Mean11.72795011
Minimum-0.66687
Maximum1750000
Zeros2786
Zeros (%)0.5%
Negative4
Negative (%)< 0.1%
Memory size4.3 MiB
2023-03-30T18:45:21.192354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.66687
5-th percentile0.7
Q12.75
median5.125
Q36.94
95-th percentile12.25
Maximum1750000
Range1750000.667
Interquartile range (IQR)4.19

Descriptive statistics

Standard deviation3292.046758
Coefficient of variation (CV)280.7009517
Kurtosis282537.9299
Mean11.72795011
Median Absolute Deviation (MAD)2.095
Skewness531.5041634
Sum3314576.717
Variance10837571.86
MonotonicityNot monotonic
2023-03-30T18:45:21.326674image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 4945
 
0.9%
6 4833
 
0.9%
4 3854
 
0.7%
3 3710
 
0.7%
2 3532
 
0.6%
5.5 3186
 
0.6%
5.25 2876
 
0.5%
7 2839
 
0.5%
0 2786
 
0.5%
1 2338
 
0.4%
Other values (33428) 247723
44.1%
(Missing) 278562
49.6%
ValueCountFrequency (%)
-0.66687 2
 
< 0.1%
-0.5 1
 
< 0.1%
-0.146 1
 
< 0.1%
0 2786
0.5%
0.001 2
 
< 0.1%
ValueCountFrequency (%)
1750000 1
< 0.1%
18902 1
< 0.1%
8077 1
< 0.1%
3924 1
< 0.1%
3125 1
< 0.1%

delivery_date
Categorical

HIGH CARDINALITY  MISSING 

Distinct11733
Distinct (%)2.2%
Missing18179
Missing (%)3.2%
Memory size4.3 MiB
2021-09-30
 
634
2021-06-30
 
604
2022-02-28
 
570
2020-02-28
 
564
2022-04-29
 
541
Other values (11728)
540092 

Unique

Unique1963 ?
Unique (%)0.4%

Sample

1st row1989-11-01
2nd row1993-10-19
3rd row1994-01-14
4th row1994-08-02
5th row1993-05-27

Common Values

ValueCountFrequency (%)
2021-09-30 634
 
0.1%
2021-06-30 604
 
0.1%
2022-02-28 570
 
0.1%
2020-02-28 564
 
0.1%
2022-04-29 541
 
0.1%
2021-01-29 538
 
0.1%
2020-01-31 534
 
0.1%
2020-09-30 528
 
0.1%
2021-02-26 517
 
0.1%
2016-06-30 517
 
0.1%
Other values (11723) 537458
95.8%
(Missing) 18179
 
3.2%

unit_deal
Categorical

Distinct2
Distinct (%)< 0.1%
Missing790
Missing (%)0.1%
Memory size4.3 MiB
N
559985 
Y
 
409

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 559985
99.8%
Y 409
 
0.1%
(Missing) 790
 
0.1%

Common Values (Plot)

2023-03-30T18:45:21.449765image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

form_of_own
Categorical

Distinct7
Distinct (%)< 0.1%
Missing328
Missing (%)0.1%
Memory size4.3 MiB
BE
552709 
R
 
7188
C
 
543
R/C
 
368
COMB
 
32
Other values (2)
 
16

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBE
2nd rowBE
3rd rowBE
4th rowBE
5th rowBE

Common Values

ValueCountFrequency (%)
BE 552709
98.5%
R 7188
 
1.3%
C 543
 
0.1%
R/C 368
 
0.1%
COMB 32
 
< 0.1%
O 14
 
< 0.1%
GS 2
 
< 0.1%
(Missing) 328
 
0.1%

Common Values (Plot)

2023-03-30T18:45:21.543685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

denomination
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct163
Distinct (%)< 0.1%
Missing672
Missing (%)0.1%
Memory size4.3 MiB
1/1
358032 
10/5
78947 
100/1
49199 
100/5
 
23007
2/1
 
19006
Other values (158)
 
32321

Unique

Unique75 ?
Unique (%)< 0.1%

Sample

1st row1/1
2nd row1/1
3rd row1/1
4th row100/1
5th row100/1

Common Values

ValueCountFrequency (%)
1/1 358032
63.8%
10/5 78947
 
14.1%
100/1 49199
 
8.8%
100/5 23007
 
4.1%
2/1 19006
 
3.4%
10/1 11314
 
2.0%
5/1 9125
 
1.6%
200/1 2833
 
0.5%
5/5 2819
 
0.5%
500/5 2151
 
0.4%
Other values (153) 4079
 
0.7%

principal_amt
Real number (ℝ)

Distinct3894
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3295.066183
Minimum0
Maximum100000000
Zeros6
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:21.657582image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q11000
median1000
Q31000
95-th percentile1000
Maximum100000000
Range100000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation317269.8886
Coefficient of variation (CV)96.28634783
Kurtosis88033.62585
Mean3295.066183
Median Absolute Deviation (MAD)0
Skewness282.5217547
Sum1849138421
Variance1.006601822 × 1011
MonotonicityNot monotonic
2023-03-30T18:45:21.786272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 496292
88.4%
10 51648
 
9.2%
100 3395
 
0.6%
25 2731
 
0.5%
10000 416
 
0.1%
50 361
 
0.1%
5000 282
 
0.1%
1000000 182
 
< 0.1%
100000 169
 
< 0.1%
2000 145
 
< 0.1%
Other values (3884) 5563
 
1.0%
ValueCountFrequency (%)
0 6
 
< 0.1%
0.5 1
 
< 0.1%
0.85 1
 
< 0.1%
1 80
< 0.1%
1.23 1
 
< 0.1%
ValueCountFrequency (%)
100000000 5
 
< 0.1%
15000000 1
 
< 0.1%
10000000 60
< 0.1%
5000000 3
 
< 0.1%
2000000 2
 
< 0.1%

covenants
Categorical

Distinct2
Distinct (%)< 0.1%
Missing648
Missing (%)0.1%
Memory size4.3 MiB
N
514773 
Y
 
45763

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowY
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 514773
91.7%
Y 45763
 
8.2%
(Missing) 648
 
0.1%

Common Values (Plot)

2023-03-30T18:45:21.898469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

defeased
Categorical

Distinct2
Distinct (%)< 0.1%
Missing928
Missing (%)0.2%
Memory size4.3 MiB
N
560157 
Y
 
99

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 560157
99.8%
Y 99
 
< 0.1%
(Missing) 928
 
0.2%

Common Values (Plot)

2023-03-30T18:45:21.984104image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

defeasance_type
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)3.8%
Missing561132
Missing (%)> 99.9%
Memory size4.3 MiB
L
49 
C
 
3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowL
2nd rowL
3rd rowL
4th rowL
5th rowL

Common Values

ValueCountFrequency (%)
L 49
 
< 0.1%
C 3
 
< 0.1%
(Missing) 561132
> 99.9%

Common Values (Plot)

2023-03-30T18:45:22.067642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

defeased_date
Categorical

HIGH CARDINALITY  MISSING 

Distinct51
Distinct (%)68.0%
Missing561109
Missing (%)> 99.9%
Memory size4.3 MiB
2001-03-31
12 
1994-10-20
 
4
1990-05-31
 
4
2000-03-31
 
3
2002-05-14
 
2
Other values (46)
50 

Unique

Unique42 ?
Unique (%)56.0%

Sample

1st row1994-12-01
2nd row1995-09-01
3rd row1995-12-01
4th row1995-06-01
5th row1996-12-24

Common Values

ValueCountFrequency (%)
2001-03-31 12
 
< 0.1%
1994-10-20 4
 
< 0.1%
1990-05-31 4
 
< 0.1%
2000-03-31 3
 
< 0.1%
2002-05-14 2
 
< 0.1%
2005-06-07 2
 
< 0.1%
1997-11-15 2
 
< 0.1%
1992-12-31 2
 
< 0.1%
2006-08-02 2
 
< 0.1%
1989-06-30 1
 
< 0.1%
Other values (41) 41
 
< 0.1%
(Missing) 561109
> 99.9%

defaulted
Categorical

Distinct2
Distinct (%)< 0.1%
Missing768
Missing (%)0.1%
Memory size4.3 MiB
N
559527 
Y
 
889

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 559527
99.7%
Y 889
 
0.2%
(Missing) 768
 
0.1%

Common Values (Plot)

2023-03-30T18:45:22.152142image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing625
Missing (%)0.1%
Memory size4.3 MiB
N
546587 
Y
 
13972

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowY

Common Values

ValueCountFrequency (%)
N 546587
97.4%
Y 13972
 
2.5%
(Missing) 625
 
0.1%

Common Values (Plot)

2023-03-30T18:45:22.234509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

redeemable
Categorical

Distinct2
Distinct (%)< 0.1%
Missing70
Missing (%)< 0.1%
Memory size4.3 MiB
Y
330655 
N
230459 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowY
5th rowY

Common Values

ValueCountFrequency (%)
Y 330655
58.9%
N 230459
41.1%
(Missing) 70
 
< 0.1%

Common Values (Plot)

2023-03-30T18:45:22.318072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing709
Missing (%)0.1%
Memory size4.3 MiB
N
557145 
Y
 
3330

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 557145
99.3%
Y 3330
 
0.6%
(Missing) 709
 
0.1%

Common Values (Plot)

2023-03-30T18:45:22.410976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

refunding_date
Categorical

HIGH CARDINALITY  MISSING 

Distinct681
Distinct (%)20.5%
Missing557859
Missing (%)99.4%
Memory size4.3 MiB
1991-12-01
 
23
1991-07-01
 
22
1991-05-01
 
20
1986-12-01
 
17
1991-06-01
 
17
Other values (676)
3226 

Unique

Unique147 ?
Unique (%)4.4%

Sample

1st row1993-04-01
2nd row1993-04-01
3rd row1997-03-01
4th row1975-03-15
5th row1975-11-01

Common Values

ValueCountFrequency (%)
1991-12-01 23
 
< 0.1%
1991-07-01 22
 
< 0.1%
1991-05-01 20
 
< 0.1%
1986-12-01 17
 
< 0.1%
1991-06-01 17
 
< 0.1%
1990-05-01 17
 
< 0.1%
1985-04-01 16
 
< 0.1%
1990-06-15 16
 
< 0.1%
1990-06-01 16
 
< 0.1%
1987-06-01 16
 
< 0.1%
Other values (671) 3145
 
0.6%
(Missing) 557859
99.4%

putable
Categorical

Distinct2
Distinct (%)< 0.1%
Missing1081
Missing (%)0.2%
Memory size4.3 MiB
N
557191 
Y
 
2912

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 557191
99.3%
Y 2912
 
0.5%
(Missing) 1081
 
0.2%

Common Values (Plot)

2023-03-30T18:45:22.489675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing927
Missing (%)0.2%
Memory size4.3 MiB
N
555788 
Y
 
4469

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 555788
99.0%
Y 4469
 
0.8%
(Missing) 927
 
0.2%

Common Values (Plot)

2023-03-30T18:45:22.571388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

announced_call
Categorical

Distinct2
Distinct (%)< 0.1%
Missing786
Missing (%)0.1%
Memory size4.3 MiB
N
560302 
Y
 
96

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 560302
99.8%
Y 96
 
< 0.1%
(Missing) 786
 
0.1%

Common Values (Plot)

2023-03-30T18:45:22.651326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

active_issue
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
N
456052 
Y
105132 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowY

Common Values

ValueCountFrequency (%)
N 456052
81.3%
Y 105132
 
18.7%

Common Values (Plot)

2023-03-30T18:45:22.736192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

dep_eligibility
Categorical

IMBALANCE  MISSING 

Distinct13
Distinct (%)< 0.1%
Missing36467
Missing (%)6.5%
Memory size4.3 MiB
DTC
305982 
FED
185544 
DCE
 
20609
CEF
 
8196
EUCD
 
2669
Other values (8)
 
1717

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDTC
2nd rowDTC
3rd rowDTC
4th rowDTC
5th rowDTC

Common Values

ValueCountFrequency (%)
DTC 305982
54.5%
FED 185544
33.1%
DCE 20609
 
3.7%
CEF 8196
 
1.5%
EUCD 2669
 
0.5%
CDS 1277
 
0.2%
DCED 166
 
< 0.1%
EUR 146
 
< 0.1%
DE 57
 
< 0.1%
DFEC 41
 
< 0.1%
Other values (3) 30
 
< 0.1%
(Missing) 36467
 
6.5%
Distinct2
Distinct (%)< 0.1%
Missing897
Missing (%)0.2%
Memory size4.3 MiB
N
559420 
Y
 
867

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 559420
99.7%
Y 867
 
0.2%
(Missing) 897
 
0.2%

Common Values (Plot)

2023-03-30T18:45:22.815942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

bond_type
Categorical

Distinct48
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
CMTN
149975 
ADEB
147844 
CMTZ
82068 
CDEB
58930 
AMTN
37516 
Other values (43)
84851 

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowCDEB
2nd rowCDEB
3rd rowCMTN
4th rowUSBN
5th rowCDEB

Common Values

ValueCountFrequency (%)
CMTN 149975
26.7%
ADEB 147844
26.3%
CMTZ 82068
14.6%
CDEB 58930
 
10.5%
AMTN 37516
 
6.7%
RNT 25155
 
4.5%
ADNT 8734
 
1.6%
USBN 8329
 
1.5%
FGOV 6963
 
1.2%
CCOV 6023
 
1.1%
Other values (38) 29647
 
5.3%

subsequent_data
Categorical

Distinct2
Distinct (%)< 0.1%
Missing119435
Missing (%)21.3%
Memory size4.3 MiB
Y
372483 
N
69266 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowY
3rd rowN
4th rowY
5th rowY

Common Values

ValueCountFrequency (%)
Y 372483
66.4%
N 69266
 
12.3%
(Missing) 119435
 
21.3%

Common Values (Plot)

2023-03-30T18:45:22.903142image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

press_release
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing559790
Missing (%)99.8%
Memory size4.3 MiB
N
1367 
Y
 
27

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowY
3rd rowY
4th rowY
5th rowN

Common Values

ValueCountFrequency (%)
N 1367
 
0.2%
Y 27
 
< 0.1%
(Missing) 559790
99.8%

Common Values (Plot)

2023-03-30T18:45:22.991291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

isin
Categorical

Distinct559035
Distinct (%)> 99.9%
Missing2138
Missing (%)0.4%
Memory size4.3 MiB
US561409AA14
 
2
XS0329579600
 
2
XS2010040637
 
2
US233293AQ29
 
2
XS1823397333
 
2
Other values (559030)
559036 

Unique

Unique559024 ?
Unique (%)> 99.9%

Sample

1st rowUS000361AA35
2nd rowUS000361AB18
3rd rowUS00077DAB55
4th rowUS00077DAF69
5th rowUS00077TAA25

Common Values

ValueCountFrequency (%)
US561409AA14 2
 
< 0.1%
XS0329579600 2
 
< 0.1%
XS2010040637 2
 
< 0.1%
US233293AQ29 2
 
< 0.1%
XS1823397333 2
 
< 0.1%
US9128202B27 2
 
< 0.1%
XS0097125693 2
 
< 0.1%
XS1200679667 2
 
< 0.1%
XS0282506657 2
 
< 0.1%
XS2193947137 2
 
< 0.1%
Other values (559025) 559026
99.6%
(Missing) 2138
 
0.4%

perpetual
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
N
558860 
Y
 
2324

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 558860
99.6%
Y 2324
 
0.4%

Common Values (Plot)

2023-03-30T18:45:23.096411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

sedol
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing561184
Missing (%)100.0%
Memory size4.3 MiB

exchangeable
Categorical

Distinct2
Distinct (%)< 0.1%
Missing927
Missing (%)0.2%
Memory size4.3 MiB
N
506080 
Y
54177 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 506080
90.2%
Y 54177
 
9.7%
(Missing) 927
 
0.2%

Common Values (Plot)

2023-03-30T18:45:23.173004image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

fungible
Categorical

Distinct2
Distinct (%)< 0.1%
Missing863
Missing (%)0.2%
Memory size4.3 MiB
N
524141 
Y
 
36180

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 524141
93.4%
Y 36180
 
6.4%
(Missing) 863
 
0.2%

Common Values (Plot)

2023-03-30T18:45:23.275107image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing556380
Missing (%)99.1%
Memory size4.3 MiB
Y
3237 
N
1567 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowY
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
Y 3237
 
0.6%
N 1567
 
0.3%
(Missing) 556380
99.1%

Common Values (Plot)

2023-03-30T18:45:23.353747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing925
Missing (%)0.2%
Memory size4.3 MiB
N
499718 
Y
60541 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 499718
89.0%
Y 60541
 
10.8%
(Missing) 925
 
0.2%

Common Values (Plot)

2023-03-30T18:45:23.433300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

complete_cusip
Categorical

HIGH CARDINALITY  UNIQUE 

Distinct561184
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
000361AA3
 
1
3130A4UG5
 
1
06741USU9
 
1
1730T06A2
 
1
3130A4UC4
 
1
Other values (561179)
561179 

Unique

Unique561184 ?
Unique (%)100.0%

Sample

1st row000361AA3
2nd row000361AB1
3rd row00077DAB5
4th row00077DAF6
5th row00077TAA2

Common Values

ValueCountFrequency (%)
000361AA3 1
 
< 0.1%
3130A4UG5 1
 
< 0.1%
06741USU9 1
 
< 0.1%
1730T06A2 1
 
< 0.1%
3130A4UC4 1
 
< 0.1%
3130A4UD2 1
 
< 0.1%
3130A4UE0 1
 
< 0.1%
3130A4UF7 1
 
< 0.1%
3130A4UH3 1
 
< 0.1%
90274Q243 1
 
< 0.1%
Other values (561174) 561174
> 99.9%

action_type
Categorical

Distinct30
Distinct (%)< 0.1%
Missing5
Missing (%)< 0.1%
Memory size4.3 MiB
IM
234252 
E
191190 
I
96795 
B
 
11523
X
 
9782
Other values (25)
 
17637

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowIM
2nd rowIM
3rd rowIM
4th rowE
5th rowX

Common Values

ValueCountFrequency (%)
IM 234252
41.7%
E 191190
34.1%
I 96795
17.2%
B 11523
 
2.1%
X 9782
 
1.7%
R 4059
 
0.7%
REV 3614
 
0.6%
IA 2750
 
0.5%
T 2345
 
0.4%
RO 1955
 
0.3%
Other values (20) 2914
 
0.5%

effective_date
Categorical

Distinct11236
Distinct (%)2.0%
Missing3315
Missing (%)0.6%
Memory size4.3 MiB
2022-05-31
 
1353
2021-01-29
 
741
2022-04-29
 
672
2021-09-30
 
663
2008-12-31
 
652
Other values (11231)
553788 

Unique

Unique1086 ?
Unique (%)0.2%

Sample

1st row2001-11-01
2nd row2003-10-15
3rd row1996-01-12
4th row2004-08-01
5th row2011-06-27

Common Values

ValueCountFrequency (%)
2022-05-31 1353
 
0.2%
2021-01-29 741
 
0.1%
2022-04-29 672
 
0.1%
2021-09-30 663
 
0.1%
2008-12-31 652
 
0.1%
2021-10-29 612
 
0.1%
2021-06-30 608
 
0.1%
2022-03-31 603
 
0.1%
2021-04-30 585
 
0.1%
2012-03-06 584
 
0.1%
Other values (11226) 550796
98.1%
(Missing) 3315
 
0.6%

action_price
Real number (ℝ)

MISSING  SKEWED 

Distinct8078
Distinct (%)4.0%
Missing359281
Missing (%)64.0%
Infinite0
Infinite (%)0.0%
Mean102.7335339
Minimum0
Maximum450000
Zeros2240
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:23.565432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile100
Q1100
median100
Q3100
95-th percentile103.75
Maximum450000
Range450000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1060.261612
Coefficient of variation (CV)10.3205017
Kurtosis161620.5436
Mean102.7335339
Median Absolute Deviation (MAD)0
Skewness388.8830975
Sum20742208.7
Variance1124154.686
MonotonicityNot monotonic
2023-03-30T18:45:23.836925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 179912
32.1%
0 2240
 
0.4%
101 272
 
< 0.1%
102 237
 
< 0.1%
103 210
 
< 0.1%
105 187
 
< 0.1%
110 186
 
< 0.1%
104 173
 
< 0.1%
104.5 144
 
< 0.1%
106 128
 
< 0.1%
Other values (8068) 18214
 
3.2%
(Missing) 359281
64.0%
ValueCountFrequency (%)
0 2240
0.4%
0.001 1
 
< 0.1%
0.01 1
 
< 0.1%
0.236 1
 
< 0.1%
0.242 1
 
< 0.1%
ValueCountFrequency (%)
450000 1
< 0.1%
108385 1
< 0.1%
106775 1
< 0.1%
29500 1
< 0.1%
22022 1
< 0.1%

action_amount
Real number (ℝ)

MISSING  SKEWED 

Distinct26857
Distinct (%)11.8%
Missing334198
Missing (%)59.6%
Infinite0
Infinite (%)0.0%
Mean138065.6632
Minimum0
Maximum1420000000
Zeros2818
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:23.966597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile220
Q19635.25
median25000
Q375000
95-th percentile400000
Maximum1420000000
Range1420000000
Interquartile range (IQR)65364.75

Descriptive statistics

Standard deviation4669770.102
Coefficient of variation (CV)33.82282021
Kurtosis62525.48791
Mean138065.6632
Median Absolute Deviation (MAD)23237
Skewness229.9638808
Sum3.133897262 × 1010
Variance2.18067528 × 1013
MonotonicityNot monotonic
2023-03-30T18:45:24.095036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25000 26414
 
4.7%
15000 20110
 
3.6%
50000 19013
 
3.4%
100000 10560
 
1.9%
20000 5814
 
1.0%
10000 5601
 
1.0%
30000 5353
 
1.0%
40000 3992
 
0.7%
250000 3945
 
0.7%
75000 3464
 
0.6%
Other values (26847) 122720
 
21.9%
(Missing) 334198
59.6%
ValueCountFrequency (%)
0 2818
0.5%
0.003 1
 
< 0.1%
0.03955 1
 
< 0.1%
0.041 1
 
< 0.1%
0.3 1
 
< 0.1%
ValueCountFrequency (%)
1420000000 1
 
< 0.1%
1250000000 1
 
< 0.1%
650000000 1
 
< 0.1%
400000000 1
 
< 0.1%
300000000 3
< 0.1%

amount_outstanding
Real number (ℝ)

SKEWED  ZEROS 

Distinct17609
Distinct (%)3.1%
Missing32
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean87510.05766
Minimum-7477500
Maximum2000000000
Zeros456041
Zeros (%)81.3%
Negative3
Negative (%)< 0.1%
Memory size4.3 MiB
2023-03-30T18:45:24.229024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-7477500
5-th percentile0
Q10
median0
Q30
95-th percentile25000
Maximum2000000000
Range2007477500
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4368242.347
Coefficient of variation (CV)49.91703199
Kurtosis150487.5979
Mean87510.05766
Median Absolute Deviation (MAD)0
Skewness349.2550159
Sum4.910644387 × 1010
Variance1.90815412 × 1013
MonotonicityNot monotonic
2023-03-30T18:45:24.360095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 456041
81.3%
500000 2373
 
0.4%
15000 2117
 
0.4%
25000 1941
 
0.3%
1000 1771
 
0.3%
500 1629
 
0.3%
1000000 1525
 
0.3%
100 1398
 
0.2%
1 1284
 
0.2%
50000 1165
 
0.2%
Other values (17599) 89908
 
16.0%
ValueCountFrequency (%)
-7477500 1
 
< 0.1%
-3478678 1
 
< 0.1%
-200 1
 
< 0.1%
0 456041
81.3%
2 × 10-51
 
< 0.1%
ValueCountFrequency (%)
2000000000 1
< 0.1%
1924515000 1
< 0.1%
1000000000 1
< 0.1%
354074000 1
< 0.1%
254787330 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing515429
Missing (%)91.8%
Memory size4.3 MiB
Y
23325 
N
22430 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowY
3rd rowN
4th rowY
5th rowY

Common Values

ValueCountFrequency (%)
Y 23325
 
4.2%
N 22430
 
4.0%
(Missing) 515429
91.8%

Common Values (Plot)

2023-03-30T18:45:24.484830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing515429
Missing (%)91.8%
Memory size4.3 MiB
Y
23298 
N
22457 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowY
5th rowY

Common Values

ValueCountFrequency (%)
Y 23298
 
4.2%
N 22457
 
4.0%
(Missing) 515429
91.8%

Common Values (Plot)

2023-03-30T18:45:24.559817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

legal_defeasance
Categorical

Distinct2
Distinct (%)< 0.1%
Missing515429
Missing (%)91.8%
Memory size4.3 MiB
N
40633 
Y
5122 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 40633
 
7.2%
Y 5122
 
0.9%
(Missing) 515429
91.8%

Common Values (Plot)

2023-03-30T18:45:24.637658image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing515429
Missing (%)91.8%
Memory size4.3 MiB
Y
24601 
N
21154 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowY
4th rowY
5th rowY

Common Values

ValueCountFrequency (%)
Y 24601
 
4.4%
N 21154
 
3.8%
(Missing) 515429
91.8%

Common Values (Plot)

2023-03-30T18:45:24.715325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

cross_default
Categorical

Distinct2
Distinct (%)< 0.1%
Missing515429
Missing (%)91.8%
Memory size4.3 MiB
N
36787 
Y
8968 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 36787
 
6.6%
Y 8968
 
1.6%
(Missing) 515429
91.8%

Common Values (Plot)

2023-03-30T18:45:24.795004image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing515429
Missing (%)91.8%
Memory size4.3 MiB
Y
28998 
N
16757 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowY
3rd rowY
4th rowY
5th rowY

Common Values

ValueCountFrequency (%)
Y 28998
 
5.2%
N 16757
 
3.0%
(Missing) 515429
91.8%

Common Values (Plot)

2023-03-30T18:45:24.876784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing515429
Missing (%)91.8%
Memory size4.3 MiB
N
25540 
Y
20215 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowN
3rd rowN
4th rowY
5th rowY

Common Values

ValueCountFrequency (%)
N 25540
 
4.6%
Y 20215
 
3.6%
(Missing) 515429
91.8%

Common Values (Plot)

2023-03-30T18:45:24.959863image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

voting_power_percentage
Real number (ℝ)

Distinct64
Distinct (%)0.3%
Missing538879
Missing (%)96.0%
Infinite0
Infinite (%)0.0%
Mean37.37027891
Minimum0
Maximum561
Zeros5321
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:25.061932image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q130
median51
Q351
95-th percentile51
Maximum561
Range561
Interquartile range (IQR)21

Descriptive statistics

Standard deviation23.08454736
Coefficient of variation (CV)0.6177247811
Kurtosis53.46548556
Mean37.37027891
Median Absolute Deviation (MAD)0
Skewness1.830394117
Sum833544.0711
Variance532.8963267
MonotonicityNot monotonic
2023-03-30T18:45:25.187883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51 12320
 
2.2%
0 5321
 
0.9%
50 2272
 
0.4%
36 1042
 
0.2%
35 297
 
0.1%
41 274
 
< 0.1%
30 190
 
< 0.1%
31 153
 
< 0.1%
40 123
 
< 0.1%
20 30
 
< 0.1%
Other values (54) 283
 
0.1%
(Missing) 538879
96.0%
ValueCountFrequency (%)
0 5321
0.9%
0.51 7
 
< 0.1%
1 1
 
< 0.1%
10 1
 
< 0.1%
11 3
 
< 0.1%
ValueCountFrequency (%)
561 1
 
< 0.1%
510 5
< 0.1%
360 2
 
< 0.1%
101 9
< 0.1%
100 4
< 0.1%

voting_power_percentage_erp
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct12
Distinct (%)0.2%
Missing554906
Missing (%)98.9%
Infinite0
Infinite (%)0.0%
Mean0.2124880535
Minimum0
Maximum510
Zeros6256
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:25.297589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum510
Range510
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.89979879
Coefficient of variation (CV)32.47146687
Kurtosis4760.060884
Mean0.2124880535
Median Absolute Deviation (MAD)0
Skewness65.35959217
Sum1334
Variance47.60722335
MonotonicityNot monotonic
2023-03-30T18:45:25.393197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 6256
 
1.1%
40 6
 
< 0.1%
30 6
 
< 0.1%
51 2
 
< 0.1%
50 1
 
< 0.1%
10 1
 
< 0.1%
60 1
 
< 0.1%
35 1
 
< 0.1%
31 1
 
< 0.1%
15 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
(Missing) 554906
98.9%
ValueCountFrequency (%)
0 6256
1.1%
10 1
 
< 0.1%
15 1
 
< 0.1%
30 6
 
< 0.1%
31 1
 
< 0.1%
ValueCountFrequency (%)
510 1
< 0.1%
101 1
< 0.1%
60 1
< 0.1%
51 2
< 0.1%
50 1
< 0.1%

rating_decline_trigger_put
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing515429
Missing (%)91.8%
Memory size4.3 MiB
N
45196 
Y
 
559

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 45196
 
8.1%
Y 559
 
0.1%
(Missing) 515429
91.8%

Common Values (Plot)

2023-03-30T18:45:25.485856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

rating_decline_provision
Categorical

HIGH CARDINALITY  MISSING 

Distinct257
Distinct (%)47.3%
Missing560641
Missing (%)99.9%
Memory size4.3 MiB
SEE GENERAL FOOTNOTE.
176 
SEE GENERAL FOOTNOTE
 
24
“CHANGE OF CONTROL TRIGGERING EVENT” MEANS THE OCCURRENCE OF BOTH A CHANGE OF CONTROL AND A BELOW INVESTMENT GRADE RATING EVENT.
 
24
“CHANGE OF CONTROL TRIGGERING EVENT” MEANS THE OCCURRENCE OF BOTH A CHANGE OF CONTROL AND A RATING EVENT.
 
13
SEE FOOTNOTE
 
6
Other values (252)
300 

Unique

Unique223 ?
Unique (%)41.1%

Sample

1st rowSEE GENERAL FOOTNOTE.
2nd rowPIK DEBENTURES MUST BE PAID AT PAR
3rd rowSEE GENERAL FOOTNOTE.
4th rowSEE GENERAL FOOTNOTE.
5th rowSEE GENERAL FOOTNOTE.

Common Values

ValueCountFrequency (%)
SEE GENERAL FOOTNOTE. 176
 
< 0.1%
SEE GENERAL FOOTNOTE 24
 
< 0.1%
“CHANGE OF CONTROL TRIGGERING EVENT” MEANS THE OCCURRENCE OF BOTH A CHANGE OF CONTROL AND A BELOW INVESTMENT GRADE RATING EVENT. 24
 
< 0.1%
“CHANGE OF CONTROL TRIGGERING EVENT” MEANS THE OCCURRENCE OF BOTH A CHANGE OF CONTROL AND A RATING EVENT. 13
 
< 0.1%
SEE FOOTNOTE 6
 
< 0.1%
SEE GENERAL FOOTNOTE FOR "CHANGE OF CONTROL EVENT" DEFINITION 5
 
< 0.1%
“CHANGE OF CONTROL TRIGGERING EVENT” MEANS THE OCCURRENCE OF BOTH A CHANGE OF CONTROL AND A RATING DECLINE. 5
 
< 0.1%
“CHANGE OF CONTROL TRIGGERING EVENT” MEANS THE NOTES CEASE TO BE RATED INVESTMENT GRADE BY EACH OF THE TWO RATING AGENCIES 5
 
< 0.1%
"CHANGE OF CONTROL TRIGGERING EVENT" MEANS THE OCCURRENCE OF BOTH A (1) CHANGE OF CONTROL AND (II) A RATING DECLINE. 4
 
< 0.1%
THE NOTES CEASE TO BE RATED INVESTMENT GRADE BY EACH OF THE RATING AGENCIES ON ANY DATE DURING THE PERIOD COMMENCING 60 DAYS FOLLOWING CONSUMMATION OF SUCH CHANGE OF CONTROL 4
 
< 0.1%
Other values (247) 277
 
< 0.1%
(Missing) 560641
99.9%

declining_net_worth
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing515429
Missing (%)91.8%
Memory size4.3 MiB
N
45410 
Y
 
345

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 45410
 
8.1%
Y 345
 
0.1%
(Missing) 515429
91.8%

Common Values (Plot)

2023-03-30T18:45:25.562910image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct94
Distinct (%)18.6%
Missing560679
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean65904715.49
Minimum0
Maximum7000000000
Zeros284
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:25.661189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q331000000
95-th percentile255000000
Maximum7000000000
Range7000000000
Interquartile range (IQR)31000000

Descriptive statistics

Standard deviation339232518.7
Coefficient of variation (CV)5.147317854
Kurtosis348.2350938
Mean65904715.49
Median Absolute Deviation (MAD)0
Skewness17.30992918
Sum3.328188132 × 1010
Variance1.150787017 × 1017
MonotonicityNot monotonic
2023-03-30T18:45:25.784523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 284
 
0.1%
25000000 11
 
< 0.1%
200000000 9
 
< 0.1%
90000000 9
 
< 0.1%
100000000 8
 
< 0.1%
10000000 8
 
< 0.1%
20000000 8
 
< 0.1%
65000000 7
 
< 0.1%
125000000 6
 
< 0.1%
15000000 6
 
< 0.1%
Other values (84) 149
 
< 0.1%
(Missing) 560679
99.9%
ValueCountFrequency (%)
0 284
0.1%
1 1
 
< 0.1%
115 1
 
< 0.1%
175 1
 
< 0.1%
1000 1
 
< 0.1%
ValueCountFrequency (%)
7000000000 1
 
< 0.1%
1000000000 5
< 0.1%
850000000 1
 
< 0.1%
750000000 1
 
< 0.1%
700000000 1
 
< 0.1%

declining_net_worth_percentage
Real number (ℝ)

MISSING  ZEROS 

Distinct28
Distinct (%)0.5%
Missing555327
Missing (%)99.0%
Infinite0
Infinite (%)0.0%
Mean0.501274714
Minimum0
Maximum99
Zeros5639
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:25.896720image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum99
Range99
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.235044504
Coefficient of variation (CV)6.453635928
Kurtosis329.6341318
Mean0.501274714
Median Absolute Deviation (MAD)0
Skewness13.94406502
Sum2935.966
Variance10.46551294
MonotonicityNot monotonic
2023-03-30T18:45:25.995054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 5639
 
1.0%
10 139
 
< 0.1%
20 31
 
< 0.1%
7.5 10
 
< 0.1%
15 9
 
< 0.1%
25 4
 
< 0.1%
99 2
 
< 0.1%
50 2
 
< 0.1%
5 2
 
< 0.1%
16 1
 
< 0.1%
Other values (18) 18
 
< 0.1%
(Missing) 555327
99.0%
ValueCountFrequency (%)
0 5639
1.0%
3 1
 
< 0.1%
3.75 1
 
< 0.1%
5 2
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
99 2
< 0.1%
50 2
< 0.1%
42.86 1
< 0.1%
30 1
< 0.1%
25.8 1
< 0.1%
Distinct49
Distinct (%)62.8%
Missing561106
Missing (%)> 99.9%
Memory size4.3 MiB
SEE GENERAL FOOTNOTE.
18 
SEE GENERAL FOOTNOTE
SEE FOOTNOTE
PURCHASE PRICE IS 100%.
 
2
THE COMPANY WILL MAKE AN OFFER FOR REPURCHASE, AT PAR, OF 10% OF THE INITIAL OUTSTANDING PRINCIPAL AMOUNT OF THE NOTES.
 
1
Other values (44)
44 

Unique

Unique45 ?
Unique (%)57.7%

Sample

1st rowPRINCIPAL AMOUNT PLUS INTEREST THEREAFTER
2nd rowSEE GENERAL FOOTNOTE.
3rd rowIF NET WORTH IS LESS THAN 125% OF THE INDEBTEDNESS OF THE COMPANY, THE NOTES WILL BE CALLABLE AT PAR.
4th rowSEE GENERAL FOOTNOTE
5th rowSEE GENERAL FOOTNOTE.

Common Values

ValueCountFrequency (%)
SEE GENERAL FOOTNOTE. 18
 
< 0.1%
SEE GENERAL FOOTNOTE 8
 
< 0.1%
SEE FOOTNOTE 5
 
< 0.1%
PURCHASE PRICE IS 100%. 2
 
< 0.1%
THE COMPANY WILL MAKE AN OFFER FOR REPURCHASE, AT PAR, OF 10% OF THE INITIAL OUTSTANDING PRINCIPAL AMOUNT OF THE NOTES. 1
 
< 0.1%
PURCHASE PRICE SHALL BE 100%. 1
 
< 0.1%
PRICE 101% 1
 
< 0.1%
11.25% OF NOTES WILL BE REPURCHASED AT A CASH PRICE EQUAL TO 100% 1
 
< 0.1%
NET WORTH DROPS TO NEGITIVE, THAN PURCHASE OFFER FOR UP TO 10% OF THE NOTES AT 101, WILL BE TRIGGERED. 1
 
< 0.1%
REPURCHASE PRICE SHALL BE 100%. 1
 
< 0.1%
Other values (39) 39
 
< 0.1%
(Missing) 561106
> 99.9%

after_acquired_property_clause
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing515429
Missing (%)91.8%
Memory size4.3 MiB
N
44985 
Y
 
770

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 44985
 
8.0%
Y 770
 
0.1%
(Missing) 515429
91.8%

Common Values (Plot)

2023-03-30T18:45:26.089185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

economic_cov_def
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing515429
Missing (%)91.8%
Memory size4.3 MiB
N
42248 
Y
 
3507

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 42248
 
7.5%
Y 3507
 
0.6%
(Missing) 515429
91.8%

Common Values (Plot)

2023-03-30T18:45:26.159994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing515429
Missing (%)91.8%
Memory size4.3 MiB
N
39330 
Y
6425 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 39330
 
7.0%
Y 6425
 
1.1%
(Missing) 515429
91.8%

Common Values (Plot)

2023-03-30T18:45:26.236131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

fix_frequency
Categorical

Distinct13
Distinct (%)< 0.1%
Missing520934
Missing (%)92.8%
Memory size4.3 MiB
Q
19554 
M
10271 
D
7408 
W
 
1284
SA
 
1072
Other values (8)
 
661

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowQ
2nd rowM
3rd rowQ
4th rowQ
5th rowQ

Common Values

ValueCountFrequency (%)
Q 19554
 
3.5%
M 10271
 
1.8%
D 7408
 
1.3%
W 1284
 
0.2%
SA 1072
 
0.2%
EFY 215
 
< 0.1%
QW 179
 
< 0.1%
A 149
 
< 0.1%
MW 111
 
< 0.1%
ETY 4
 
< 0.1%
Other values (3) 3
 
< 0.1%
(Missing) 520934
92.8%

determination_date
Categorical

HIGH CARDINALITY  MISSING 

Distinct7739
Distinct (%)19.3%
Missing521183
Missing (%)92.9%
Memory size4.3 MiB
2022-07-01
 
427
2022-07-05
 
117
2020-03-31
 
102
2022-09-13
 
81
2022-08-11
 
66
Other values (7734)
39208 

Unique

Unique1696 ?
Unique (%)4.2%

Sample

1st row1994-08-02
2nd row2013-04-25
3rd row1997-11-10
4th row1998-10-08
5th row1998-10-12

Common Values

ValueCountFrequency (%)
2022-07-01 427
 
0.1%
2022-07-05 117
 
< 0.1%
2020-03-31 102
 
< 0.1%
2022-09-13 81
 
< 0.1%
2022-08-11 66
 
< 0.1%
2022-07-27 54
 
< 0.1%
2022-08-25 50
 
< 0.1%
2022-07-13 47
 
< 0.1%
2022-09-28 45
 
< 0.1%
2022-07-21 42
 
< 0.1%
Other values (7729) 38970
 
6.9%
(Missing) 521183
92.9%

greater_of
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing445449
Missing (%)79.4%
Memory size4.3 MiB
N
115698 
Y
 
37

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 115698
 
20.6%
Y 37
 
< 0.1%
(Missing) 445449
79.4%

Common Values (Plot)

2023-03-30T18:45:26.327492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

lesser_of
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing445449
Missing (%)79.4%
Memory size4.3 MiB
N
115724 
Y
 
11

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 115724
 
20.6%
Y 11
 
< 0.1%
(Missing) 445449
79.4%

Common Values (Plot)

2023-03-30T18:45:26.526608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

see_note
Categorical

Distinct2
Distinct (%)< 0.1%
Missing445447
Missing (%)79.4%
Memory size4.3 MiB
Y
77514 
N
38223 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowY
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
Y 77514
 
13.8%
N 38223
 
6.8%
(Missing) 445447
79.4%

Common Values (Plot)

2023-03-30T18:45:26.689790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

reset_date
Categorical

HIGH CARDINALITY  MISSING 

Distinct8964
Distinct (%)22.5%
Missing521267
Missing (%)92.9%
Memory size4.3 MiB
2022-07-05
 
409
2022-07-06
 
130
2022-09-15
 
82
2022-08-15
 
67
2022-07-29
 
61
Other values (8959)
39168 

Unique

Unique2232 ?
Unique (%)5.6%

Sample

1st row2013-04-29
2nd row1997-11-12
3rd row1998-10-12
4th row1998-10-14
5th row2003-05-15

Common Values

ValueCountFrequency (%)
2022-07-05 409
 
0.1%
2022-07-06 130
 
< 0.1%
2022-09-15 82
 
< 0.1%
2022-08-15 67
 
< 0.1%
2022-07-29 61
 
< 0.1%
2022-07-15 46
 
< 0.1%
2022-08-30 45
 
< 0.1%
2022-09-30 45
 
< 0.1%
2022-07-25 41
 
< 0.1%
2021-06-15 36
 
< 0.1%
Other values (8954) 38955
 
6.9%
(Missing) 521267
92.9%

determination_date_orig
Categorical

HIGH CARDINALITY  MISSING 

Distinct8093
Distinct (%)20.1%
Missing520932
Missing (%)92.8%
Memory size4.3 MiB
1998-09-14
 
239
2008-11-20
 
61
1998-09-01
 
45
1998-08-18
 
44
2008-03-31
 
33
Other values (8088)
39830 

Unique

Unique1603 ?
Unique (%)4.0%

Sample

1st row1994-08-02
2nd row1995-01-26
3rd row1997-11-10
4th row1999-01-07
5th row1994-04-12

Common Values

ValueCountFrequency (%)
1998-09-14 239
 
< 0.1%
2008-11-20 61
 
< 0.1%
1998-09-01 45
 
< 0.1%
1998-08-18 44
 
< 0.1%
2008-03-31 33
 
< 0.1%
1998-08-19 31
 
< 0.1%
1998-08-24 30
 
< 0.1%
2019-01-30 29
 
< 0.1%
2007-01-23 29
 
< 0.1%
2005-03-21 29
 
< 0.1%
Other values (8083) 39682
 
7.1%
(Missing) 520932
92.8%

reset_date_orig
Categorical

HIGH CARDINALITY  MISSING 

Distinct8127
Distinct (%)20.2%
Missing521026
Missing (%)92.8%
Memory size4.3 MiB
1998-09-15
 
244
2008-11-24
 
63
1998-09-01
 
53
1998-08-18
 
44
1998-08-24
 
36
Other values (8122)
39718 

Unique

Unique1756 ?
Unique (%)4.4%

Sample

1st row1995-01-30
2nd row1997-11-12
3rd row1999-01-11
4th row1994-04-14
5th row1993-11-15

Common Values

ValueCountFrequency (%)
1998-09-15 244
 
< 0.1%
2008-11-24 63
 
< 0.1%
1998-09-01 53
 
< 0.1%
1998-08-18 44
 
< 0.1%
1998-08-24 36
 
< 0.1%
2006-02-15 35
 
< 0.1%
2006-12-15 34
 
< 0.1%
2006-05-15 34
 
< 0.1%
2006-11-15 34
 
< 0.1%
2019-01-30 33
 
< 0.1%
Other values (8117) 39548
 
7.0%
(Missing) 521026
92.8%

conv_commod_cusip
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing555538
Missing (%)99.0%
Memory size4.3 MiB

conv_commod_issuer
Categorical

HIGH CARDINALITY  MISSING 

Distinct3644
Distinct (%)6.1%
Missing501489
Missing (%)89.4%
Memory size4.3 MiB
APPLE INCORPORATED
 
2356
AMAZON COM INCORPORATED
 
1272
NETFLIX INCORPORATED
 
1255
BANK OF AMERICA CORPORATION
 
1042
FACEBOOK INCORPORATED
 
969
Other values (3639)
52801 

Unique

Unique1490 ?
Unique (%)2.5%

Sample

1st rowAFG INDUSTRIES INCORPORATED
2nd rowAG SERVICES OF AMERICA INCORPORATED
3rd rowAES CORPORATION
4th rowBMD CLASS A COMMON STOCK
5th rowAMERICAN AIRLINES GROUP INCORPORATED

Common Values

ValueCountFrequency (%)
APPLE INCORPORATED 2356
 
0.4%
AMAZON COM INCORPORATED 1272
 
0.2%
NETFLIX INCORPORATED 1255
 
0.2%
BANK OF AMERICA CORPORATION 1042
 
0.2%
FACEBOOK INCORPORATED 969
 
0.2%
FREEPORT-MCMORAN INCORPORATED 800
 
0.1%
FORD MOTOR COMPANY 798
 
0.1%
TESLA INCORPORATED 780
 
0.1%
UNITED STATES STEEL CORPORATION 761
 
0.1%
NVIDIA CORPORATION 723
 
0.1%
Other values (3634) 48939
 
8.7%
(Missing) 501489
89.4%

conv_commod_type
Categorical

IMBALANCE  MISSING 

Distinct8
Distinct (%)< 0.1%
Missing501992
Missing (%)89.5%
Memory size4.3 MiB
CS
55118 
ADS
 
2437
CSA
 
1182
USD
 
239
CSB
 
155
Other values (3)
 
61

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCS
2nd rowCS
3rd rowCS
4th rowCS
5th rowCS

Common Values

ValueCountFrequency (%)
CS 55118
 
9.8%
ADS 2437
 
0.4%
CSA 1182
 
0.2%
USD 239
 
< 0.1%
CSB 155
 
< 0.1%
RS 24
 
< 0.1%
DEB 22
 
< 0.1%
PS 15
 
< 0.1%
(Missing) 501992
89.5%

Common Values (Plot)

2023-03-30T18:45:26.838537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

exchange
Categorical

IMBALANCE  MISSING 

Distinct36
Distinct (%)0.1%
Missing502251
Missing (%)89.5%
Memory size4.3 MiB
N
35521 
NAS
20785 
ARCA
 
1842
A
 
307
OOTC
 
132
Other values (31)
 
346

Unique

Unique8 ?
Unique (%)< 0.1%

Sample

1st rowNAS
2nd rowNAS
3rd rowN
4th rowNAS
5th rowA

Common Values

ValueCountFrequency (%)
N 35521
 
6.3%
NAS 20785
 
3.7%
ARCA 1842
 
0.3%
A 307
 
0.1%
OOTC 132
 
< 0.1%
OTC 123
 
< 0.1%
T 62
 
< 0.1%
NMS 49
 
< 0.1%
TAI 19
 
< 0.1%
H 14
 
< 0.1%
Other values (26) 79
 
< 0.1%
(Missing) 502251
89.5%

ticker
Categorical

HIGH CARDINALITY  MISSING 

Distinct3263
Distinct (%)5.5%
Missing502372
Missing (%)89.5%
Memory size4.3 MiB
AAPL
 
2330
AMZN
 
1270
NFLX
 
1253
FB
 
1043
BAC
 
1037
Other values (3258)
51879 

Unique

Unique1239 ?
Unique (%)2.1%

Sample

1st rowAGSV
2nd rowAESC
3rd rowAMR
4th rowASTA L
5th rowAMI

Common Values

ValueCountFrequency (%)
AAPL 2330
 
0.4%
AMZN 1270
 
0.2%
NFLX 1253
 
0.2%
FB 1043
 
0.2%
BAC 1037
 
0.2%
FCX 983
 
0.2%
TSLA 801
 
0.1%
F 798
 
0.1%
X 766
 
0.1%
NVDA 719
 
0.1%
Other values (3253) 47812
 
8.5%
(Missing) 502372
89.5%

conv_price
Real number (ℝ)

MISSING  SKEWED 

Distinct4393
Distinct (%)69.8%
Missing554886
Missing (%)98.9%
Infinite0
Infinite (%)0.0%
Mean94.37054175
Minimum0
Maximum89605.73477
Zeros175
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:27.002047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.6400425
Q114.5
median28.50782
Q350.55817
95-th percentile127.7899295
Maximum89605.73477
Range89605.73477
Interquartile range (IQR)36.05817

Descriptive statistics

Standard deviation1724.73453
Coefficient of variation (CV)18.27619613
Kurtosis2365.326596
Mean94.37054175
Median Absolute Deviation (MAD)16.492525
Skewness47.14630946
Sum594345.6719
Variance2974709.197
MonotonicityNot monotonic
2023-03-30T18:45:27.149236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 175
 
< 0.1%
40 24
 
< 0.1%
20 23
 
< 0.1%
50 19
 
< 0.1%
12 17
 
< 0.1%
25 17
 
< 0.1%
5 16
 
< 0.1%
18 16
 
< 0.1%
15 14
 
< 0.1%
30 13
 
< 0.1%
Other values (4383) 5964
 
1.1%
(Missing) 554886
98.9%
ValueCountFrequency (%)
0 175
< 0.1%
0.01924 1
 
< 0.1%
0.15 1
 
< 0.1%
0.26476 1
 
< 0.1%
0.30328 1
 
< 0.1%
ValueCountFrequency (%)
89605.73477 2
< 0.1%
42669 1
< 0.1%
16271.27475 1
< 0.1%
15909.3802 1
< 0.1%
12323.81 1
< 0.1%

qty_of_commod
Real number (ℝ)

Distinct4674
Distinct (%)73.9%
Missing554860
Missing (%)98.9%
Infinite0
Infinite (%)0.0%
Mean98.62415203
Minimum0
Maximum8568.98029
Zeros32
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:27.285483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.712954
Q115.753
median31.99388
Q365.081925
95-th percentile333.2805
Maximum8568.98029
Range8568.98029
Interquartile range (IQR)49.328925

Descriptive statistics

Standard deviation314.2455423
Coefficient of variation (CV)3.186293984
Kurtosis175.9652348
Mean98.62415203
Median Absolute Deviation (MAD)20.12503
Skewness10.5894086
Sum623699.1374
Variance98750.26087
MonotonicityNot monotonic
2023-03-30T18:45:27.429226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 32
 
< 0.1%
50 22
 
< 0.1%
25 21
 
< 0.1%
20 18
 
< 0.1%
200 15
 
< 0.1%
100 12
 
< 0.1%
40 11
 
< 0.1%
31.25 10
 
< 0.1%
142.85714 8
 
< 0.1%
1 8
 
< 0.1%
Other values (4664) 6167
 
1.1%
(Missing) 554860
98.9%
ValueCountFrequency (%)
0 32
< 0.1%
0.00031 1
 
< 0.1%
0.0089 1
 
< 0.1%
0.01571 1
 
< 0.1%
0.02939 1
 
< 0.1%
ValueCountFrequency (%)
8568.98029 1
< 0.1%
6666.66667 1
< 0.1%
5749.8074 1
< 0.1%
5123.16 1
< 0.1%
4274.04975 1
< 0.1%

percent_of_outstanding_commod
Real number (ℝ)

MISSING  SKEWED 

Distinct5135
Distinct (%)93.1%
Missing555668
Missing (%)99.0%
Infinite0
Infinite (%)0.0%
Mean155.668514
Minimum0
Maximum635929
Zeros291
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:27.846370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.7824775
median8.96711
Q315.3370375
95-th percentile32.41708
Maximum635929
Range635929
Interquartile range (IQR)11.55456

Descriptive statistics

Standard deviation8744.556539
Coefficient of variation (CV)56.17421478
Kurtosis5078.30376
Mean155.668514
Median Absolute Deviation (MAD)5.625645
Skewness70.32664758
Sum858667.5232
Variance76467269.06
MonotonicityNot monotonic
2023-03-30T18:45:28.032293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 291
 
0.1%
6.43783 4
 
< 0.1%
100 4
 
< 0.1%
4.87155 4
 
< 0.1%
15.14129 2
 
< 0.1%
0.78018 2
 
< 0.1%
100.00023 2
 
< 0.1%
16 2
 
< 0.1%
0.00092 2
 
< 0.1%
4.14143 2
 
< 0.1%
Other values (5125) 5201
 
0.9%
(Missing) 555668
99.0%
ValueCountFrequency (%)
0 291
0.1%
1 × 10-52
 
< 0.1%
4 × 10-51
 
< 0.1%
8 × 10-51
 
< 0.1%
0.00011 2
 
< 0.1%
ValueCountFrequency (%)
635929 1
< 0.1%
131494 1
< 0.1%
11482.82979 1
< 0.1%
2856.28178 1
< 0.1%
1833.79004 1
< 0.1%

conv_cash
Real number (ℝ)

MISSING  SKEWED 

Distinct7
Distinct (%)0.5%
Missing559908
Missing (%)99.8%
Infinite0
Infinite (%)0.0%
Mean21.875
Minimum0
Maximum10000
Zeros1175
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:28.164026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile50
Maximum10000
Range10000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation398.7291776
Coefficient of variation (CV)18.22761955
Kurtosis615.1622008
Mean21.875
Median Absolute Deviation (MAD)0
Skewness24.65621726
Sum27912.5
Variance158984.9571
MonotonicityNot monotonic
2023-03-30T18:45:28.268642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 1175
 
0.2%
50 47
 
< 0.1%
25 30
 
< 0.1%
100 18
 
< 0.1%
1000 3
 
< 0.1%
10000 2
 
< 0.1%
12.5 1
 
< 0.1%
(Missing) 559908
99.8%
ValueCountFrequency (%)
0 1175
0.2%
12.5 1
 
< 0.1%
25 30
 
< 0.1%
50 47
 
< 0.1%
100 18
 
< 0.1%
ValueCountFrequency (%)
10000 2
 
< 0.1%
1000 3
 
< 0.1%
100 18
 
< 0.1%
50 47
< 0.1%
25 30
< 0.1%

conv_eff_date
Categorical

HIGH CARDINALITY  MISSING 

Distinct6884
Distinct (%)11.5%
Missing501500
Missing (%)89.4%
Memory size4.3 MiB
2008-04-30
 
269
2008-02-28
 
243
2010-09-30
 
223
2008-08-29
 
207
2008-07-31
 
199
Other values (6879)
58543 

Unique

Unique1892 ?
Unique (%)3.2%

Sample

1st row1986-07-29
2nd row1993-04-30
3rd row1992-03-19
4th row1989-06-22
5th row1991-03-15

Common Values

ValueCountFrequency (%)
2008-04-30 269
 
< 0.1%
2008-02-28 243
 
< 0.1%
2010-09-30 223
 
< 0.1%
2008-08-29 207
 
< 0.1%
2008-07-31 199
 
< 0.1%
2008-09-30 194
 
< 0.1%
2010-10-29 191
 
< 0.1%
2008-11-28 191
 
< 0.1%
2011-04-29 177
 
< 0.1%
2008-05-30 176
 
< 0.1%
Other values (6874) 57614
 
10.3%
(Missing) 501500
89.4%

conv_exp_date
Categorical

HIGH CARDINALITY  MISSING 

Distinct5826
Distinct (%)9.8%
Missing501705
Missing (%)89.4%
Memory size4.3 MiB
2008-04-30
 
269
2008-02-28
 
245
2010-09-30
 
227
2008-08-29
 
207
2008-07-31
 
201
Other values (5821)
58330 

Unique

Unique1058 ?
Unique (%)1.8%

Sample

1st row2006-07-15
2nd row2003-05-30
3rd row2002-03-15
4th row2014-06-15
5th row2006-03-15

Common Values

ValueCountFrequency (%)
2008-04-30 269
 
< 0.1%
2008-02-28 245
 
< 0.1%
2010-09-30 227
 
< 0.1%
2008-08-29 207
 
< 0.1%
2008-07-31 201
 
< 0.1%
2008-09-30 196
 
< 0.1%
2010-10-29 192
 
< 0.1%
2008-11-28 190
 
< 0.1%
2011-04-29 187
 
< 0.1%
2008-05-30 179
 
< 0.1%
Other values (5816) 57386
 
10.2%
(Missing) 501705
89.4%

dilution_protection
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing501639
Missing (%)89.4%
Memory size4.3 MiB
Y
59468 
N
 
77

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowY
3rd rowY
4th rowY
5th rowY

Common Values

ValueCountFrequency (%)
Y 59468
 
10.6%
N 77
 
< 0.1%
(Missing) 501639
89.4%

Common Values (Plot)

2023-03-30T18:45:28.385322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

commod_price
Real number (ℝ)

MISSING  SKEWED 

Distinct18665
Distinct (%)31.6%
Missing502072
Missing (%)89.5%
Infinite0
Infinite (%)0.0%
Mean136.3495576
Minimum0
Maximum143500
Zeros255
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:28.487820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.6555
Q123.24
median45.17
Q399.92
95-th percentile450.5465
Maximum143500
Range143500
Interquartile range (IQR)76.68

Descriptive statistics

Standard deviation798.4007368
Coefficient of variation (CV)5.855543288
Kurtosis19503.74155
Mean136.3495576
Median Absolute Deviation (MAD)27.83
Skewness120.8609144
Sum8059895.048
Variance637443.7365
MonotonicityNot monotonic
2023-03-30T18:45:28.620129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 255
 
< 0.1%
92.22429 48
 
< 0.1%
30.94 33
 
< 0.1%
10.5 29
 
< 0.1%
23.25 26
 
< 0.1%
88.52 25
 
< 0.1%
32 25
 
< 0.1%
16 24
 
< 0.1%
12.5 24
 
< 0.1%
47 22
 
< 0.1%
Other values (18655) 58601
 
10.4%
(Missing) 502072
89.5%
ValueCountFrequency (%)
0 255
< 0.1%
0.0001 1
 
< 0.1%
0.002 1
 
< 0.1%
0.0055 1
 
< 0.1%
0.01 5
 
< 0.1%
ValueCountFrequency (%)
143500 1
< 0.1%
69300 2
< 0.1%
22025 1
< 0.1%
10005.05 1
< 0.1%
3719.34 2
< 0.1%

conv_premium
Real number (ℝ)

MISSING  SKEWED 

Distinct5313
Distinct (%)85.6%
Missing554976
Missing (%)98.9%
Infinite0
Infinite (%)0.0%
Mean6805.627923
Minimum-5755417
Maximum20249906
Zeros252
Zeros (%)< 0.1%
Negative870
Negative (%)0.2%
Memory size4.3 MiB
2023-03-30T18:45:28.763360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-5755417
5-th percentile-35.2468325
Q111.7533975
median25.000015
Q344.4043975
95-th percentile818.4284585
Maximum20249906
Range26005323
Interquartile range (IQR)32.651

Descriptive statistics

Standard deviation346468.8905
Coefficient of variation (CV)50.90917317
Kurtosis2815.261541
Mean6805.627923
Median Absolute Deviation (MAD)15.86997
Skewness50.47190035
Sum42249338.14
Variance1.200406921 × 1011
MonotonicityNot monotonic
2023-03-30T18:45:28.903765image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 252
 
< 0.1%
25 70
 
< 0.1%
20 56
 
< 0.1%
30 22
 
< 0.1%
12 17
 
< 0.1%
22 15
 
< 0.1%
15 12
 
< 0.1%
10 11
 
< 0.1%
23 9
 
< 0.1%
28 8
 
< 0.1%
Other values (5303) 5736
 
1.0%
(Missing) 554976
98.9%
ValueCountFrequency (%)
-5755417 1
< 0.1%
-98.92753 1
< 0.1%
-94.71021 1
< 0.1%
-92.71831 1
< 0.1%
-91.06004 1
< 0.1%
ValueCountFrequency (%)
20249906 1
< 0.1%
16961383 1
< 0.1%
3611899 1
< 0.1%
793008.2069 1
< 0.1%
358240.2254 1
< 0.1%

conv_redemp_exception
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing521805
Missing (%)93.0%
Memory size4.3 MiB
N
38124 
Y
 
1255

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowY
3rd rowN
4th rowN
5th rowY

Common Values

ValueCountFrequency (%)
N 38124
 
6.8%
Y 1255
 
0.2%
(Missing) 521805
93.0%

Common Values (Plot)

2023-03-30T18:45:29.030906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

conv_redemp_date
Categorical

HIGH CARDINALITY  MISSING 

Distinct984
Distinct (%)61.5%
Missing559584
Missing (%)99.7%
Memory size4.3 MiB
2026-09-15
 
10
2025-05-31
 
10
2025-10-15
 
8
1988-07-15
 
8
2026-03-15
 
8
Other values (979)
1556 

Unique

Unique633 ?
Unique (%)39.6%

Sample

1st row1988-07-15
2nd row1996-05-31
3rd row1993-03-15
4th row1994-01-15
5th row1985-03-15

Common Values

ValueCountFrequency (%)
2026-09-15 10
 
< 0.1%
2025-05-31 10
 
< 0.1%
2025-10-15 8
 
< 0.1%
1988-07-15 8
 
< 0.1%
2026-03-15 8
 
< 0.1%
2024-06-01 8
 
< 0.1%
2024-11-15 7
 
< 0.1%
2025-06-14 7
 
< 0.1%
2025-04-14 7
 
< 0.1%
2025-08-15 7
 
< 0.1%
Other values (974) 1520
 
0.3%
(Missing) 559584
99.7%

conv_price_percent
Real number (ℝ)

Distinct36
Distinct (%)1.8%
Missing559140
Missing (%)99.6%
Infinite0
Infinite (%)0.0%
Mean111.7218821
Minimum0
Maximum1258
Zeros384
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:29.125428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1125
median130
Q3140
95-th percentile150
Maximum1258
Range1258
Interquartile range (IQR)15

Descriptive statistics

Standard deviation60.85773707
Coefficient of variation (CV)0.5447253119
Kurtosis61.12723836
Mean111.7218821
Median Absolute Deviation (MAD)10
Skewness2.244567363
Sum228359.527
Variance3703.664161
MonotonicityNot monotonic
2023-03-30T18:45:29.247450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
130 797
 
0.1%
150 436
 
0.1%
0 384
 
0.1%
140 186
 
< 0.1%
125 90
 
< 0.1%
120 43
 
< 0.1%
135 16
 
< 0.1%
200 14
 
< 0.1%
175 12
 
< 0.1%
115 10
 
< 0.1%
Other values (26) 56
 
< 0.1%
(Missing) 559140
99.6%
ValueCountFrequency (%)
0 384
0.1%
1.4 1
 
< 0.1%
12.07 1
 
< 0.1%
15 2
 
< 0.1%
34.65 1
 
< 0.1%
ValueCountFrequency (%)
1258 1
 
< 0.1%
300 1
 
< 0.1%
280 1
 
< 0.1%
250 2
 
< 0.1%
200 14
< 0.1%

conv_part_trade_days
Real number (ℝ)

Distinct12
Distinct (%)0.6%
Missing559153
Missing (%)99.6%
Infinite0
Infinite (%)0.0%
Mean16.57262432
Minimum0
Maximum60
Zeros380
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:29.353773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q120
median20
Q320
95-th percentile20
Maximum60
Range60
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.385391881
Coefficient of variation (CV)0.5059785172
Kurtosis0.9344273969
Mean16.57262432
Median Absolute Deviation (MAD)0
Skewness-1.105443768
Sum33659
Variance70.314797
MonotonicityNot monotonic
2023-03-30T18:45:29.443565image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
20 1551
 
0.3%
0 380
 
0.1%
30 75
 
< 0.1%
5 6
 
< 0.1%
10 4
 
< 0.1%
15 4
 
< 0.1%
2 2
 
< 0.1%
25 2
 
< 0.1%
1 2
 
< 0.1%
60 2
 
< 0.1%
Other values (2) 3
 
< 0.1%
(Missing) 559153
99.6%
ValueCountFrequency (%)
0 380
0.1%
1 2
 
< 0.1%
2 2
 
< 0.1%
5 6
 
< 0.1%
10 4
 
< 0.1%
ValueCountFrequency (%)
60 2
 
< 0.1%
45 1
 
< 0.1%
30 75
 
< 0.1%
25 2
 
< 0.1%
20 1551
0.3%

conv_total_trade_days
Real number (ℝ)

Distinct11
Distinct (%)0.5%
Missing559153
Missing (%)99.6%
Infinite0
Infinite (%)0.0%
Mean24.06794682
Minimum0
Maximum60
Zeros384
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:29.544691image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q130
median30
Q330
95-th percentile30
Maximum60
Range60
Interquartile range (IQR)0

Descriptive statistics

Standard deviation11.95563687
Coefficient of variation (CV)0.496745192
Kurtosis0.4344250118
Mean24.06794682
Median Absolute Deviation (MAD)0
Skewness-1.3751446
Sum48882
Variance142.9372529
MonotonicityNot monotonic
2023-03-30T18:45:29.642400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
30 1567
 
0.3%
0 384
 
0.1%
20 60
 
< 0.1%
60 6
 
< 0.1%
10 4
 
< 0.1%
45 2
 
< 0.1%
35 2
 
< 0.1%
1 2
 
< 0.1%
40 2
 
< 0.1%
5 1
 
< 0.1%
(Missing) 559153
99.6%
ValueCountFrequency (%)
0 384
0.1%
1 2
 
< 0.1%
5 1
 
< 0.1%
10 4
 
< 0.1%
20 60
 
< 0.1%
ValueCountFrequency (%)
60 6
 
< 0.1%
45 2
 
< 0.1%
40 2
 
< 0.1%
35 2
 
< 0.1%
30 1567
0.3%

conv_period_spec
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing521828
Missing (%)93.0%
Memory size4.3 MiB
N
38029 
Y
 
1327

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowN
3rd rowN
4th rowY
5th rowN

Common Values

ValueCountFrequency (%)
N 38029
 
6.8%
Y 1327
 
0.2%
(Missing) 521828
93.0%

Common Values (Plot)

2023-03-30T18:45:29.751043image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

conv_period_days
Real number (ℝ)

Distinct17
Distinct (%)1.2%
Missing559819
Missing (%)99.8%
Infinite0
Infinite (%)0.0%
Mean2.684249084
Minimum0
Maximum40
Zeros70
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:29.836937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile10
Maximum40
Range40
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.941555374
Coefficient of variation (CV)1.468401497
Kurtosis19.05463854
Mean2.684249084
Median Absolute Deviation (MAD)0
Skewness3.744541357
Sum3664
Variance15.53585876
MonotonicityNot monotonic
2023-03-30T18:45:29.936016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 799
 
0.1%
2 178
 
< 0.1%
5 157
 
< 0.1%
0 70
 
< 0.1%
15 50
 
< 0.1%
3 33
 
< 0.1%
7 27
 
< 0.1%
10 25
 
< 0.1%
20 8
 
< 0.1%
6 6
 
< 0.1%
Other values (7) 12
 
< 0.1%
(Missing) 559819
99.8%
ValueCountFrequency (%)
0 70
 
< 0.1%
1 799
0.1%
2 178
 
< 0.1%
3 33
 
< 0.1%
4 2
 
< 0.1%
ValueCountFrequency (%)
40 1
 
< 0.1%
35 1
 
< 0.1%
30 4
 
< 0.1%
20 8
 
< 0.1%
15 50
< 0.1%

agent_id
Real number (ℝ)

Distinct3314
Distinct (%)5.6%
Missing501853
Missing (%)89.4%
Infinite0
Infinite (%)0.0%
Mean35097.9592
Minimum3
Maximum78189
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:30.060235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile263
Q13100
median43940
Q355223
95-th percentile71511
Maximum78189
Range78186
Interquartile range (IQR)52123

Descriptive statistics

Standard deviation25156.81824
Coefficient of variation (CV)0.7167601427
Kurtosis-1.424255694
Mean35097.9592
Median Absolute Deviation (MAD)16342
Skewness-0.2521835507
Sum2082397017
Variance632865504
MonotonicityNot monotonic
2023-03-30T18:45:30.198325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
263 2356
 
0.4%
35097 1272
 
0.2%
54201 1255
 
0.2%
427 1042
 
0.2%
59842 969
 
0.2%
1689 800
 
0.1%
43867 798
 
0.1%
60010 780
 
0.1%
46203 761
 
0.1%
43940 723
 
0.1%
Other values (3304) 48575
 
8.7%
(Missing) 501853
89.4%
ValueCountFrequency (%)
3 10
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
11 18
< 0.1%
13 2
 
< 0.1%
ValueCountFrequency (%)
78189 1
< 0.1%
78111 1
< 0.1%
78105 1
< 0.1%
78054 2
< 0.1%
78049 1
< 0.1%

shares_outstanding
Real number (ℝ)

Distinct4509
Distinct (%)87.5%
Missing556029
Missing (%)99.1%
Infinite0
Infinite (%)0.0%
Mean207500991.8
Minimum0
Maximum9930000000
Zeros245
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:30.341462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile307214
Q133708933.5
median71310000
Q3166504500
95-th percentile763518200
Maximum9930000000
Range9930000000
Interquartile range (IQR)132795566.5

Descriptive statistics

Standard deviation528298434.3
Coefficient of variation (CV)2.546004382
Kurtosis83.27822737
Mean207500991.8
Median Absolute Deviation (MAD)47879570
Skewness7.774307836
Sum1.069667613 × 1012
Variance2.790992357 × 1017
MonotonicityNot monotonic
2023-03-30T18:45:30.471281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 245
 
< 0.1%
204385177 11
 
< 0.1%
118817800 6
 
< 0.1%
169576043 5
 
< 0.1%
569382412 5
 
< 0.1%
2202402441 4
 
< 0.1%
163206598 4
 
< 0.1%
250730000 4
 
< 0.1%
623004728 4
 
< 0.1%
92358140 4
 
< 0.1%
Other values (4499) 4863
 
0.9%
(Missing) 556029
99.1%
ValueCountFrequency (%)
0 245
< 0.1%
0.01143 1
 
< 0.1%
83.85506 1
 
< 0.1%
1337.25139 1
 
< 0.1%
33960 1
 
< 0.1%
ValueCountFrequency (%)
9930000000 1
< 0.1%
8763839000 1
< 0.1%
6718000000 1
< 0.1%
6690000000 1
< 0.1%
6670000000 1
< 0.1%

orig_conv_price
Real number (ℝ)

MISSING  SKEWED 

Distinct4106
Distinct (%)64.6%
Missing554832
Missing (%)98.9%
Infinite0
Infinite (%)0.0%
Mean83.69747807
Minimum0
Maximum89605.73477
Zeros33
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:30.599757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.0036465
Q114.90715
median29.188515
Q352.0824625
95-th percentile121.843565
Maximum89605.73477
Range89605.73477
Interquartile range (IQR)37.1753125

Descriptive statistics

Standard deviation1687.300288
Coefficient of variation (CV)20.15951169
Kurtosis2559.140501
Mean83.69747807
Median Absolute Deviation (MAD)17.060675
Skewness49.64510861
Sum531646.3807
Variance2846982.261
MonotonicityNot monotonic
2023-03-30T18:45:30.721278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 33
 
< 0.1%
20 24
 
< 0.1%
40 24
 
< 0.1%
50 21
 
< 0.1%
25 19
 
< 0.1%
12 18
 
< 0.1%
15 17
 
< 0.1%
5 16
 
< 0.1%
30 16
 
< 0.1%
10 15
 
< 0.1%
Other values (4096) 6149
 
1.1%
(Missing) 554832
98.9%
ValueCountFrequency (%)
0 33
< 0.1%
0.01924 1
 
< 0.1%
0.0618 1
 
< 0.1%
0.1134 1
 
< 0.1%
0.137 1
 
< 0.1%
ValueCountFrequency (%)
89605.73477 2
< 0.1%
42669 1
< 0.1%
12323.81 1
< 0.1%
4051 1
< 0.1%
2623 1
< 0.1%

orig_commod_price
Real number (ℝ)

MISSING  SKEWED 

Distinct18738
Distinct (%)31.7%
Missing502065
Missing (%)89.5%
Infinite0
Infinite (%)0.0%
Mean137.3035399
Minimum0
Maximum143500
Zeros43
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:30.877367image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.31
Q123.51
median45.55
Q3101.2
95-th percentile460.25
Maximum143500
Range143500
Interquartile range (IQR)77.69

Descriptive statistics

Standard deviation811.6687443
Coefficient of variation (CV)5.911491758
Kurtosis18789.89986
Mean137.3035399
Median Absolute Deviation (MAD)27.97
Skewness119.4230657
Sum8117247.973
Variance658806.1505
MonotonicityNot monotonic
2023-03-30T18:45:31.021912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 43
 
< 0.1%
20 27
 
< 0.1%
32 26
 
< 0.1%
16 25
 
< 0.1%
14 24
 
< 0.1%
27.75 24
 
< 0.1%
47 23
 
< 0.1%
18 22
 
< 0.1%
15.42 22
 
< 0.1%
12.4 22
 
< 0.1%
Other values (18728) 58861
 
10.5%
(Missing) 502065
89.5%
ValueCountFrequency (%)
0 43
< 0.1%
0.13 1
 
< 0.1%
0.215 1
 
< 0.1%
0.25702 1
 
< 0.1%
0.26 1
 
< 0.1%
ValueCountFrequency (%)
143500 1
< 0.1%
77900 1
< 0.1%
69300 1
< 0.1%
22025 1
< 0.1%
10005.05 1
< 0.1%

orig_conv_premium
Real number (ℝ)

MISSING  SKEWED 

Distinct5135
Distinct (%)82.6%
Missing554964
Missing (%)98.9%
Infinite0
Infinite (%)0.0%
Mean49.70623161
Minimum-98.92753
Maximum56874.603
Zeros75
Zeros (%)< 0.1%
Negative495
Negative (%)0.1%
Memory size4.3 MiB
2023-03-30T18:45:31.160553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-98.92753
5-th percentile-12.6684665
Q116.78336
median25.00002
Q337.903595
95-th percentile111.8185155
Maximum56874.603
Range56973.53053
Interquartile range (IQR)21.120235

Descriptive statistics

Standard deviation730.0187152
Coefficient of variation (CV)14.68666386
Kurtosis5906.221194
Mean49.70623161
Median Absolute Deviation (MAD)10.000125
Skewness75.92180339
Sum309172.7606
Variance532927.3246
MonotonicityNot monotonic
2023-03-30T18:45:31.277731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 100
 
< 0.1%
20 79
 
< 0.1%
0 75
 
< 0.1%
30 41
 
< 0.1%
22 26
 
< 0.1%
15 19
 
< 0.1%
12 18
 
< 0.1%
40 13
 
< 0.1%
18 13
 
< 0.1%
10 13
 
< 0.1%
Other values (5125) 5823
 
1.0%
(Missing) 554964
98.9%
ValueCountFrequency (%)
-98.92753 1
< 0.1%
-94 1
< 0.1%
-91.06004 1
< 0.1%
-90.84512 1
< 0.1%
-90.29972 1
< 0.1%
ValueCountFrequency (%)
56874.603 1
< 0.1%
3154.75099 1
< 0.1%
2583.09456 1
< 0.1%
2539.21569 1
< 0.1%
2403.7036 1
< 0.1%

orig_shares_outstanding
Real number (ℝ)

Distinct4405
Distinct (%)93.2%
Missing556456
Missing (%)99.2%
Infinite0
Infinite (%)0.0%
Mean197199128
Minimum0
Maximum9930000000
Zeros20
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:31.410275image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14729922.85
Q137233497
median71336235.5
Q3161152165.5
95-th percentile691221850
Maximum9930000000
Range9930000000
Interquartile range (IQR)123918668.5

Descriptive statistics

Standard deviation503943648
Coefficient of variation (CV)2.555506473
Kurtosis102.0902515
Mean197199128
Median Absolute Deviation (MAD)43235991
Skewness8.573955758
Sum9.32357477 × 1011
Variance2.539592004 × 1017
MonotonicityNot monotonic
2023-03-30T18:45:31.542428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20
 
< 0.1%
204385177 12
 
< 0.1%
250730000 6
 
< 0.1%
163206598 4
 
< 0.1%
37702000 4
 
< 0.1%
1207011573 4
 
< 0.1%
169576000 4
 
< 0.1%
55500000 4
 
< 0.1%
40013574 4
 
< 0.1%
2202402441 4
 
< 0.1%
Other values (4395) 4662
 
0.8%
(Missing) 556456
99.2%
ValueCountFrequency (%)
0 20
< 0.1%
1337.25139 1
 
< 0.1%
11600 1
 
< 0.1%
33960 1
 
< 0.1%
68600 1
 
< 0.1%
ValueCountFrequency (%)
9930000000 1
< 0.1%
8763839000 1
< 0.1%
6718000000 1
< 0.1%
6690000000 1
< 0.1%
6670000000 1
< 0.1%

orig_percent_outstanding_com
Real number (ℝ)

MISSING  SKEWED 

Distinct5261
Distinct (%)97.1%
Missing555766
Missing (%)99.0%
Infinite0
Infinite (%)0.0%
Mean58.93208649
Minimum0
Maximum209017
Zeros46
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:31.671667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.2679785
Q15.93768
median10.551
Q316.7881575
95-th percentile36.4378005
Maximum209017
Range209017
Interquartile range (IQR)10.8504775

Descriptive statistics

Standard deviation2845.240575
Coefficient of variation (CV)48.27999049
Kurtosis5373.338857
Mean58.93208649
Median Absolute Deviation (MAD)5.207585
Skewness73.16424642
Sum319294.0446
Variance8095393.932
MonotonicityNot monotonic
2023-03-30T18:45:31.791486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 46
 
< 0.1%
100 4
 
< 0.1%
6.43783 4
 
< 0.1%
4.87155 4
 
< 0.1%
4.06426 3
 
< 0.1%
3 3
 
< 0.1%
9.34762 2
 
< 0.1%
27 2
 
< 0.1%
6.18927 2
 
< 0.1%
7.61531 2
 
< 0.1%
Other values (5251) 5346
 
1.0%
(Missing) 555766
99.0%
ValueCountFrequency (%)
0 46
< 0.1%
0.00055 1
 
< 0.1%
0.00103 1
 
< 0.1%
0.00114 1
 
< 0.1%
0.00159 1
 
< 0.1%
ValueCountFrequency (%)
209017 1
< 0.1%
11482.82979 1
< 0.1%
3486.4565 1
< 0.1%
3387.27436 1
< 0.1%
2856.28178 1
< 0.1%

orig_qty_of_commod
Real number (ℝ)

MISSING  SKEWED 

Distinct4319
Distinct (%)68.6%
Missing554885
Missing (%)98.9%
Infinite0
Infinite (%)0.0%
Mean84.92237215
Minimum0
Maximum55762
Zeros26
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:31.914327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.862884
Q115.05715
median30.75787
Q362.24705
95-th percentile216.07956
Maximum55762
Range55762
Interquartile range (IQR)47.1899

Descriptive statistics

Standard deviation929.0746951
Coefficient of variation (CV)10.94028195
Kurtosis2980.035176
Mean84.92237215
Median Absolute Deviation (MAD)19.24213
Skewness53.26868872
Sum534926.0222
Variance863179.7891
MonotonicityNot monotonic
2023-03-30T18:45:32.341915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 26
 
< 0.1%
50 22
 
< 0.1%
20 21
 
< 0.1%
25 21
 
< 0.1%
100 14
 
< 0.1%
40 14
 
< 0.1%
200 14
 
< 0.1%
1 13
 
< 0.1%
62.5 12
 
< 0.1%
31.25 11
 
< 0.1%
Other values (4309) 6131
 
1.1%
(Missing) 554885
98.9%
ValueCountFrequency (%)
0 26
< 0.1%
0.0089 1
 
< 0.1%
0.1 2
 
< 0.1%
0.10979 1
 
< 0.1%
0.1116 2
 
< 0.1%
ValueCountFrequency (%)
55762 1
< 0.1%
45809 1
< 0.1%
7299.27 1
< 0.1%
4606 1
< 0.1%
4444.44444 1
< 0.1%

as_of_date
Categorical

HIGH CARDINALITY  MISSING 

Distinct5892
Distinct (%)9.9%
Missing501452
Missing (%)89.4%
Memory size4.3 MiB
2010-03-26
 
278
2010-04-27
 
249
2008-06-25
 
248
2010-01-26
 
246
2008-05-27
 
227
Other values (5887)
58484 

Unique

Unique1515 ?
Unique (%)2.5%

Sample

1st row1986-07-22
2nd row1993-04-22
3rd row1992-03-12
4th row1989-06-15
5th row1991-03-08

Common Values

ValueCountFrequency (%)
2010-03-26 278
 
< 0.1%
2010-04-27 249
 
< 0.1%
2008-06-25 248
 
< 0.1%
2010-01-26 246
 
< 0.1%
2008-05-27 227
 
< 0.1%
2008-03-26 224
 
< 0.1%
2007-10-26 221
 
< 0.1%
2008-01-28 214
 
< 0.1%
2008-07-28 201
 
< 0.1%
2010-02-23 197
 
< 0.1%
Other values (5882) 57427
 
10.2%
(Missing) 501452
89.4%

reason
Categorical

IMBALANCE  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing502854
Missing (%)89.6%
Memory size4.3 MiB
I
54719 
REV
 
2608
AM
 
407
NC
 
340
TC
 
256

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNC
2nd rowREV
3rd rowREV
4th rowNC
5th rowAM

Common Values

ValueCountFrequency (%)
I 54719
 
9.8%
REV 2608
 
0.5%
AM 407
 
0.1%
NC 340
 
0.1%
TC 256
 
< 0.1%
(Missing) 502854
89.6%

Common Values (Plot)

2023-03-30T18:45:32.461498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

change_date
Categorical

HIGH CARDINALITY  MISSING 

Distinct6044
Distinct (%)10.1%
Missing501452
Missing (%)89.4%
Memory size4.3 MiB
2010-03-26
 
278
2010-04-27
 
249
2008-06-25
 
248
2010-01-26
 
247
2008-05-27
 
227
Other values (6039)
58483 

Unique

Unique1614 ?
Unique (%)2.7%

Sample

1st row1986-07-22
2nd row1993-04-22
3rd row1992-03-12
4th row1989-06-15
5th row1991-03-08

Common Values

ValueCountFrequency (%)
2010-03-26 278
 
< 0.1%
2010-04-27 249
 
< 0.1%
2008-06-25 248
 
< 0.1%
2010-01-26 247
 
< 0.1%
2008-05-27 227
 
< 0.1%
2008-03-26 224
 
< 0.1%
2007-10-26 221
 
< 0.1%
2008-01-28 214
 
< 0.1%
2008-07-28 201
 
< 0.1%
2010-02-23 197
 
< 0.1%
Other values (6034) 57426
 
10.2%
(Missing) 501452
89.4%

split_date
Categorical

HIGH CARDINALITY  MISSING 

Distinct310
Distinct (%)47.4%
Missing560530
Missing (%)99.9%
Memory size4.3 MiB
2014-06-09
 
48
2007-12-04
 
23
2015-09-30
 
17
2008-07-24
 
16
2020-04-23
 
14
Other values (305)
536 

Unique

Unique215 ?
Unique (%)32.9%

Sample

1st row1999-01-19
2nd row2006-12-12
3rd row2005-08-02
4th row1999-10-22
5th row2002-01-16

Common Values

ValueCountFrequency (%)
2014-06-09 48
 
< 0.1%
2007-12-04 23
 
< 0.1%
2015-09-30 17
 
< 0.1%
2008-07-24 16
 
< 0.1%
2020-04-23 14
 
< 0.1%
2008-05-28 14
 
< 0.1%
2019-06-03 12
 
< 0.1%
2014-04-21 11
 
< 0.1%
2016-04-08 10
 
< 0.1%
2007-08-21 9
 
< 0.1%
Other values (300) 480
 
0.1%
(Missing) 560530
99.9%

split_ratio
Categorical

Distinct41
Distinct (%)6.3%
Missing560530
Missing (%)99.9%
Memory size4.3 MiB
2:1
275 
3:2
51 
7:1
48 
3:1
38 
1:10
35 
Other values (36)
207 

Unique

Unique15 ?
Unique (%)2.3%

Sample

1st row2:1
2nd row2:1
3rd row2:1
4th row2:1
5th row2:1

Common Values

ValueCountFrequency (%)
2:1 275
 
< 0.1%
3:2 51
 
< 0.1%
7:1 48
 
< 0.1%
3:1 38
 
< 0.1%
1:10 35
 
< 0.1%
1:3 28
 
< 0.1%
1:15 26
 
< 0.1%
1:4 20
 
< 0.1%
1:50 18
 
< 0.1%
1:20 14
 
< 0.1%
Other values (31) 101
 
< 0.1%
(Missing) 560530
99.9%

conditional_conv_terms
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing522172
Missing (%)93.0%
Memory size4.3 MiB
N
36991 
Y
 
2021

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 36991
 
6.6%
Y 2021
 
0.4%
(Missing) 522172
93.0%

Common Values (Plot)

2023-03-30T18:45:32.557362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

soft_call_make_whole
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing523231
Missing (%)93.2%
Memory size4.3 MiB
N
37883 
Y
 
70

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowY

Common Values

ValueCountFrequency (%)
N 37883
 
6.8%
Y 70
 
< 0.1%
(Missing) 523231
93.2%

Common Values (Plot)

2023-03-30T18:45:32.641865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

peps
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing523207
Missing (%)93.2%
Memory size4.3 MiB
N
37804 
Y
 
173

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowY
4th rowN
5th rowY

Common Values

ValueCountFrequency (%)
N 37804
 
6.7%
Y 173
 
< 0.1%
(Missing) 523207
93.2%

Common Values (Plot)

2023-03-30T18:45:32.725778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

percs
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing523299
Missing (%)93.2%
Memory size4.3 MiB
N
37880 
Y
 
5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowY
4th rowN
5th rowY

Common Values

ValueCountFrequency (%)
N 37880
 
6.8%
Y 5
 
< 0.1%
(Missing) 523299
93.2%

Common Values (Plot)

2023-03-30T18:45:32.809958image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

conv_prohibited_from
Categorical

HIGH CARDINALITY  MISSING 

Distinct1140
Distinct (%)68.6%
Missing559523
Missing (%)99.7%
Memory size4.3 MiB
2024-03-20
 
19
2023-06-20
 
13
2023-09-20
 
11
2023-06-05
 
11
2024-02-20
 
10
Other values (1135)
1597 

Unique

Unique841 ?
Unique (%)50.6%

Sample

1st row1986-07-29
2nd row1993-04-30
3rd row1991-03-15
4th row1992-01-31
5th row1983-03-15

Common Values

ValueCountFrequency (%)
2024-03-20 19
 
< 0.1%
2023-06-20 13
 
< 0.1%
2023-09-20 11
 
< 0.1%
2023-06-05 11
 
< 0.1%
2024-02-20 10
 
< 0.1%
2023-12-20 9
 
< 0.1%
2023-05-20 9
 
< 0.1%
2024-11-20 8
 
< 0.1%
2024-05-20 7
 
< 0.1%
2023-05-06 7
 
< 0.1%
Other values (1130) 1557
 
0.3%
(Missing) 559523
99.7%

convert_on_call
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing522837
Missing (%)93.2%
Memory size4.3 MiB
N
37537 
Y
 
810

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowY
3rd rowY
4th rowY
5th rowY

Common Values

ValueCountFrequency (%)
N 37537
 
6.7%
Y 810
 
0.1%
(Missing) 522837
93.2%

Common Values (Plot)

2023-03-30T18:45:32.898202image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

coco_start_date
Categorical

HIGH CARDINALITY  MISSING 

Distinct1365
Distinct (%)63.5%
Missing559034
Missing (%)99.6%
Memory size4.3 MiB
2019-12-31
 
22
2021-07-01
 
20
2018-09-30
 
19
2011-07-01
 
13
2019-06-30
 
12
Other values (1360)
2064 

Unique

Unique965 ?
Unique (%)44.9%

Sample

1st row2001-01-08
2nd row2001-02-28
3rd row2001-02-02
4th row2001-04-11
5th row2001-05-23

Common Values

ValueCountFrequency (%)
2019-12-31 22
 
< 0.1%
2021-07-01 20
 
< 0.1%
2018-09-30 19
 
< 0.1%
2011-07-01 13
 
< 0.1%
2019-06-30 12
 
< 0.1%
2022-04-01 12
 
< 0.1%
2019-09-30 11
 
< 0.1%
2003-06-30 10
 
< 0.1%
2018-12-31 10
 
< 0.1%
2003-09-30 9
 
< 0.1%
Other values (1355) 2012
 
0.4%
(Missing) 559034
99.6%

coco_end_date
Categorical

HIGH CARDINALITY  MISSING 

Distinct819
Distinct (%)38.6%
Missing559063
Missing (%)99.6%
Memory size4.3 MiB
2023-08-01
 
16
2023-07-15
 
14
2024-11-30
 
13
2018-05-14
 
12
2023-05-15
 
12
Other values (814)
2054 

Unique

Unique348 ?
Unique (%)16.4%

Sample

1st row2020-11-17
2nd row2008-03-01
3rd row2031-02-07
4th row2021-02-12
5th row2031-03-31

Common Values

ValueCountFrequency (%)
2023-08-01 16
 
< 0.1%
2023-07-15 14
 
< 0.1%
2024-11-30 13
 
< 0.1%
2018-05-14 12
 
< 0.1%
2023-05-15 12
 
< 0.1%
2025-05-31 12
 
< 0.1%
2025-08-14 12
 
< 0.1%
2024-02-15 11
 
< 0.1%
2021-12-14 11
 
< 0.1%
2026-09-14 11
 
< 0.1%
Other values (809) 1997
 
0.4%
(Missing) 559063
99.6%
Distinct20
Distinct (%)1.0%
Missing559137
Missing (%)99.6%
Infinite0
Infinite (%)0.0%
Mean126.7126673
Minimum98
Maximum220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:32.977166image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum98
5-th percentile110
Q1120
median130
Q3130
95-th percentile130
Maximum220
Range122
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.519910328
Coefficient of variation (CV)0.05934616078
Kurtosis24.86654738
Mean126.7126673
Median Absolute Deviation (MAD)0
Skewness1.765359717
Sum259380.83
Variance56.54905134
MonotonicityNot monotonic
2023-03-30T18:45:33.082061image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
130 1276
 
0.2%
120 510
 
0.1%
110 109
 
< 0.1%
125 68
 
< 0.1%
150 33
 
< 0.1%
135 15
 
< 0.1%
140 12
 
< 0.1%
115 6
 
< 0.1%
100 4
 
< 0.1%
220 2
 
< 0.1%
Other values (10) 12
 
< 0.1%
(Missing) 559137
99.6%
ValueCountFrequency (%)
98 1
 
< 0.1%
100 4
 
< 0.1%
110 109
< 0.1%
114.71 1
 
< 0.1%
115 6
 
< 0.1%
ValueCountFrequency (%)
220 2
 
< 0.1%
175 1
 
< 0.1%
160 1
 
< 0.1%
156.25 1
 
< 0.1%
150 33
< 0.1%

coco_trigger_expressed_as
Categorical

IMBALANCE  MISSING 

Distinct5
Distinct (%)0.2%
Missing559142
Missing (%)99.6%
Memory size4.3 MiB
CP
1934 
ACP
 
88
AV
 
15
ISP
 
4
BMP
 
1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowACP
2nd rowCP
3rd rowACP
4th rowACP
5th rowACP

Common Values

ValueCountFrequency (%)
CP 1934
 
0.3%
ACP 88
 
< 0.1%
AV 15
 
< 0.1%
ISP 4
 
< 0.1%
BMP 1
 
< 0.1%
(Missing) 559142
99.6%

Common Values (Plot)

2023-03-30T18:45:33.194976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

coco_change_rate
Real number (ℝ)

Distinct24
Distinct (%)1.6%
Missing559660
Missing (%)99.7%
Infinite0
Infinite (%)0.0%
Mean0.7059317585
Minimum-1.11
Maximum150
Zeros1473
Zeros (%)0.3%
Negative25
Negative (%)< 0.1%
Memory size4.3 MiB
2023-03-30T18:45:33.287200image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1.11
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum150
Range151.11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9.451496746
Coefficient of variation (CV)13.3886833
Kurtosis188.3489324
Mean0.7059317585
Median Absolute Deviation (MAD)0
Skewness13.71701298
Sum1075.84
Variance89.33079073
MonotonicityNot monotonic
2023-03-30T18:45:33.386346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 1473
 
0.3%
-0.5 13
 
< 0.1%
0.5 8
 
< 0.1%
130 6
 
< 0.1%
-0.334 2
 
< 0.1%
-0.513 2
 
< 0.1%
20 2
 
< 0.1%
-0.336 2
 
< 0.1%
-1.11 1
 
< 0.1%
150 1
 
< 0.1%
Other values (14) 14
 
< 0.1%
(Missing) 559660
99.7%
ValueCountFrequency (%)
-1.11 1
 
< 0.1%
-0.513 2
 
< 0.1%
-0.512 1
 
< 0.1%
-0.5 13
< 0.1%
-0.4 1
 
< 0.1%
ValueCountFrequency (%)
150 1
 
< 0.1%
130 6
< 0.1%
110 1
 
< 0.1%
20 2
 
< 0.1%
1 1
 
< 0.1%

coco_min_trigger_level
Real number (ℝ)

Distinct22
Distinct (%)1.2%
Missing559366
Missing (%)99.7%
Infinite0
Infinite (%)0.0%
Mean126.001104
Minimum0
Maximum220
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:33.491431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile110
Q1120
median130
Q3130
95-th percentile130
Maximum220
Range220
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.795210432
Coefficient of variation (CV)0.06980264581
Kurtosis38.00995019
Mean126.001104
Median Absolute Deviation (MAD)0
Skewness-0.4217115419
Sum229070.007
Variance77.35572653
MonotonicityNot monotonic
2023-03-30T18:45:33.580313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
130 1061
 
0.2%
120 462
 
0.1%
110 137
 
< 0.1%
125 63
 
< 0.1%
150 32
 
< 0.1%
135 14
 
< 0.1%
140 12
 
< 0.1%
115 10
 
< 0.1%
98 6
 
< 0.1%
100 5
 
< 0.1%
Other values (12) 16
 
< 0.1%
(Missing) 559366
99.7%
ValueCountFrequency (%)
0 1
 
< 0.1%
97 1
 
< 0.1%
98 6
< 0.1%
100 5
< 0.1%
109.827 1
 
< 0.1%
ValueCountFrequency (%)
220 2
 
< 0.1%
175 1
 
< 0.1%
160 1
 
< 0.1%
156.25 1
 
< 0.1%
150 32
< 0.1%

coco_change_frequency
Categorical

IMBALANCE  MISSING 

Distinct4
Distinct (%)0.2%
Missing559156
Missing (%)99.6%
Memory size4.3 MiB
NONE
1753 
Q
252 
A
 
16
S
 
7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNONE
2nd rowNONE
3rd rowQ
4th rowNONE
5th rowQ

Common Values

ValueCountFrequency (%)
NONE 1753
 
0.3%
Q 252
 
< 0.1%
A 16
 
< 0.1%
S 7
 
< 0.1%
(Missing) 559156
99.6%

Common Values (Plot)

2023-03-30T18:45:33.685580image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

coco_trade_days
Real number (ℝ)

Distinct4
Distinct (%)0.2%
Missing559164
Missing (%)99.6%
Infinite0
Infinite (%)0.0%
Mean2016.832673
Minimum11
Maximum2030
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:33.766052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile2030
Q12030
median2030
Q32030
95-th percentile2030
Maximum2030
Range2019
Interquartile range (IQR)0

Descriptive statistics

Standard deviation156.7529097
Coefficient of variation (CV)0.07772231768
Kurtosis157.0237707
Mean2016.832673
Median Absolute Deviation (MAD)0
Skewness-12.52814301
Sum4074002
Variance24571.47471
MonotonicityNot monotonic
2023-03-30T18:45:33.842233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
2030 1971
 
0.4%
2020 33
 
< 0.1%
11 12
 
< 0.1%
1520 4
 
< 0.1%
(Missing) 559164
99.6%
ValueCountFrequency (%)
11 12
 
< 0.1%
1520 4
 
< 0.1%
2020 33
 
< 0.1%
2030 1971
0.4%
ValueCountFrequency (%)
2030 1971
0.4%
2020 33
 
< 0.1%
1520 4
 
< 0.1%
11 12
 
< 0.1%

coco_trade_days_in_previous
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)0.1%
Missing559161
Missing (%)99.6%
Memory size4.3 MiB
Q
1944 
PD
 
74
S
 
5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPD
2nd rowQ
3rd rowQ
4th rowPD
5th rowQ

Common Values

ValueCountFrequency (%)
Q 1944
 
0.3%
PD 74
 
< 0.1%
S 5
 
< 0.1%
(Missing) 559161
99.6%

Common Values (Plot)

2023-03-30T18:45:33.946206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct44
Distinct (%)97.8%
Missing561139
Missing (%)> 99.9%
Memory size4.3 MiB
2013-02-03
 
2
2004-02-24
 
1
2003-08-12
 
1
2003-10-30
 
1
2004-03-16
 
1
Other values (39)
39 

Unique

Unique43 ?
Unique (%)95.6%

Sample

1st row1999-06-13
2nd row2000-09-02
3rd row2001-11-27
4th row2002-02-01
5th row2002-02-20

Common Values

ValueCountFrequency (%)
2013-02-03 2
 
< 0.1%
2004-02-24 1
 
< 0.1%
2003-08-12 1
 
< 0.1%
2003-10-30 1
 
< 0.1%
2004-03-16 1
 
< 0.1%
2006-06-18 1
 
< 0.1%
2004-03-04 1
 
< 0.1%
2004-02-27 1
 
< 0.1%
2004-05-26 1
 
< 0.1%
2004-05-21 1
 
< 0.1%
Other values (34) 34
 
< 0.1%
(Missing) 561139
> 99.9%
Distinct35
Distinct (%)77.8%
Missing561139
Missing (%)> 99.9%
Memory size4.3 MiB
2007-03-06
 
3
2010-07-15
 
2
2006-06-30
 
2
2014-03-15
 
2
2008-08-15
 
2
Other values (30)
34 

Unique

Unique26 ?
Unique (%)57.8%

Sample

1st row2002-02-06
2nd row2003-02-07
3rd row2004-06-21
4th row2005-02-01
5th row2005-02-20

Common Values

ValueCountFrequency (%)
2007-03-06 3
 
< 0.1%
2010-07-15 2
 
< 0.1%
2006-06-30 2
 
< 0.1%
2014-03-15 2
 
< 0.1%
2008-08-15 2
 
< 0.1%
2015-03-01 2
 
< 0.1%
2005-02-01 2
 
< 0.1%
2004-06-21 2
 
< 0.1%
2003-02-07 2
 
< 0.1%
2016-08-15 1
 
< 0.1%
Other values (25) 25
 
< 0.1%
(Missing) 561139
> 99.9%
Distinct3
Distinct (%)7.1%
Missing561142
Missing (%)> 99.9%
Memory size4.3 MiB
CP
22 
CPV
19 
A
 
1

Unique

Unique1 ?
Unique (%)2.4%

Sample

1st rowCP
2nd rowCP
3rd rowCP
4th rowCP
5th rowCPV

Common Values

ValueCountFrequency (%)
CP 22
 
< 0.1%
CPV 19
 
< 0.1%
A 1
 
< 0.1%
(Missing) 561142
> 99.9%

Common Values (Plot)

2023-03-30T18:45:34.042534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct15
Distinct (%)71.4%
Missing561163
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean140.6142857
Minimum60
Maximum236.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:34.123168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile75
Q1112.94
median130.1
Q3165
95-th percentile236.9
Maximum236.9
Range176.9
Interquartile range (IQR)52.06

Descriptive statistics

Standard deviation48.53163088
Coefficient of variation (CV)0.345140116
Kurtosis-0.09901469759
Mean140.6142857
Median Absolute Deviation (MAD)34.9
Skewness0.5111300165
Sum2952.9
Variance2355.319196
MonotonicityNot monotonic
2023-03-30T18:45:34.205045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
165 4
 
< 0.1%
120 2
 
< 0.1%
130.1 2
 
< 0.1%
236.9 2
 
< 0.1%
212.6 1
 
< 0.1%
172.5 1
 
< 0.1%
105 1
 
< 0.1%
86.25 1
 
< 0.1%
135 1
 
< 0.1%
112.94 1
 
< 0.1%
Other values (5) 5
 
< 0.1%
(Missing) 561163
> 99.9%
ValueCountFrequency (%)
60 1
< 0.1%
75 1
< 0.1%
86.25 1
< 0.1%
90 1
< 0.1%
105 1
< 0.1%
ValueCountFrequency (%)
236.9 2
< 0.1%
212.6 1
 
< 0.1%
172.5 1
 
< 0.1%
165 4
< 0.1%
150 1
 
< 0.1%
Distinct15
Distinct (%)71.4%
Missing561163
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean3.869047619
Minimum1.625
Maximum5.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:34.295474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.625
5-th percentile1.875
Q12.875
median4
Q35
95-th percentile5.75
Maximum5.75
Range4.125
Interquartile range (IQR)2.125

Descriptive statistics

Standard deviation1.372996376
Coefficient of variation (CV)0.3548667556
Kurtosis-1.323001728
Mean3.869047619
Median Absolute Deviation (MAD)1.125
Skewness-0.1370450908
Sum81.25
Variance1.885119048
MonotonicityNot monotonic
2023-03-30T18:45:34.374726image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
5.5 3
 
< 0.1%
5.75 2
 
< 0.1%
4 2
 
< 0.1%
4.25 2
 
< 0.1%
2.875 2
 
< 0.1%
4.75 1
 
< 0.1%
3.5 1
 
< 0.1%
4.5 1
 
< 0.1%
2.25 1
 
< 0.1%
3 1
 
< 0.1%
Other values (5) 5
 
< 0.1%
(Missing) 561163
> 99.9%
ValueCountFrequency (%)
1.625 1
< 0.1%
1.875 1
< 0.1%
2 1
< 0.1%
2.25 1
< 0.1%
2.5 1
< 0.1%
ValueCountFrequency (%)
5.75 2
< 0.1%
5.5 3
< 0.1%
5 1
 
< 0.1%
4.75 1
 
< 0.1%
4.5 1
 
< 0.1%

peps_max_conversion_ratio
Real number (ℝ)

Distinct155
Distinct (%)89.1%
Missing561010
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean11.44116483
Minimum0.1973
Maximum571.42857
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:34.479291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.1973
5-th percentile0.4721
Q10.91545
median1.4233
Q33.5676
95-th percentile41.860455
Maximum571.42857
Range571.23127
Interquartile range (IQR)2.65215

Descriptive statistics

Standard deviation51.6618402
Coefficient of variation (CV)4.515435358
Kurtosis85.50399782
Mean11.44116483
Median Absolute Deviation (MAD)0.7399
Skewness8.695690753
Sum1990.76268
Variance2668.945733
MonotonicityNot monotonic
2023-03-30T18:45:34.596066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 11
 
< 0.1%
9.0909 2
 
< 0.1%
1.8182 2
 
< 0.1%
0.9001 2
 
< 0.1%
2.1739 2
 
< 0.1%
1.3513 2
 
< 0.1%
3.5714 2
 
< 0.1%
0.6231 2
 
< 0.1%
1.2821 2
 
< 0.1%
0.5997 2
 
< 0.1%
Other values (145) 145
 
< 0.1%
(Missing) 561010
> 99.9%
ValueCountFrequency (%)
0.1973 1
< 0.1%
0.2833 1
< 0.1%
0.28835 1
< 0.1%
0.3325 1
< 0.1%
0.342 1
< 0.1%
ValueCountFrequency (%)
571.42857 1
< 0.1%
259.7402 1
< 0.1%
250 1
< 0.1%
71.4286 1
< 0.1%
62.5 1
< 0.1%

peps_min_conversion_ratio
Real number (ℝ)

Distinct166
Distinct (%)95.4%
Missing561010
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean9.232984195
Minimum0.1579
Maximum444.44444
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:34.722195image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.1579
5-th percentile0.416285
Q10.766
median1.169
Q33.017525
95-th percentile34.371065
Maximum444.44444
Range444.28654
Interquartile range (IQR)2.251525

Descriptive statistics

Standard deviation40.77911151
Coefficient of variation (CV)4.416677279
Kurtosis81.60072621
Mean9.232984195
Median Absolute Deviation (MAD)0.59845
Skewness8.502169718
Sum1606.53925
Variance1662.935936
MonotonicityNot monotonic
2023-03-30T18:45:34.840023image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8475 3
 
< 0.1%
0.5082 2
 
< 0.1%
0.833 2
 
< 0.1%
0.8333 2
 
< 0.1%
0.5258 2
 
< 0.1%
0.766 2
 
< 0.1%
1.0811 2
 
< 0.1%
0.5038 1
 
< 0.1%
1.2355 1
 
< 0.1%
185.5288 1
 
< 0.1%
Other values (156) 156
 
< 0.1%
(Missing) 561010
> 99.9%
ValueCountFrequency (%)
0.1579 1
< 0.1%
0.2354 1
< 0.1%
0.2361 1
< 0.1%
0.2406 1
< 0.1%
0.266 1
< 0.1%
ValueCountFrequency (%)
444.44444 1
< 0.1%
227.2727 1
< 0.1%
185.5288 1
< 0.1%
58.548 1
< 0.1%
50.813 1
< 0.1%

peps_higher_price
Real number (ℝ)

Distinct169
Distinct (%)97.1%
Missing561010
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean56.30011592
Minimum1.968
Maximum708.01473
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:34.964120image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.968
5-th percentile5.533
Q122.27
median38.66534
Q365.2274175
95-th percentile133.572306
Maximum708.01473
Range706.04673
Interquartile range (IQR)42.9574175

Descriptive statistics

Standard deviation72.20613154
Coefficient of variation (CV)1.282521898
Kurtosis40.93382712
Mean56.30011592
Median Absolute Deviation (MAD)19.776185
Skewness5.408761605
Sum9796.22017
Variance5213.725432
MonotonicityNot monotonic
2023-03-30T18:45:35.086841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.548 2
 
< 0.1%
65.27415 2
 
< 0.1%
92.49838 2
 
< 0.1%
22.27 2
 
< 0.1%
49.19 2
 
< 0.1%
43.74836 1
 
< 0.1%
7.11724 1
 
< 0.1%
80.58018 1
 
< 0.1%
80.93889 1
 
< 0.1%
5.39 1
 
< 0.1%
Other values (159) 159
 
< 0.1%
(Missing) 561010
> 99.9%
ValueCountFrequency (%)
1.968 1
< 0.1%
2 1
< 0.1%
2.25 1
< 0.1%
2.415 1
< 0.1%
3.11 1
< 0.1%
ValueCountFrequency (%)
708.01473 1
< 0.1%
352.79591 1
< 0.1%
330.00033 1
< 0.1%
312.23 1
< 0.1%
211.77467 1
< 0.1%

peps_lower_price
Real number (ℝ)

Distinct166
Distinct (%)95.4%
Missing561010
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean46.18260368
Minimum1.6
Maximum590.00531
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:35.215925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.6
5-th percentile4.537
Q117.97
median31.29003
Q355.274535
95-th percentile111.31273
Maximum590.00531
Range588.40531
Interquartile range (IQR)37.304535

Descriptive statistics

Standard deviation59.85118547
Coefficient of variation (CV)1.295968194
Kurtosis42.15696658
Mean46.18260368
Median Absolute Deviation (MAD)16.40541
Skewness5.512770264
Sum8035.77304
Variance3582.164402
MonotonicityNot monotonic
2023-03-30T18:45:35.335968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27.5 3
 
< 0.1%
40.125 2
 
< 0.1%
14.00011 2
 
< 0.1%
55.54938 2
 
< 0.1%
74.00281 2
 
< 0.1%
16 2
 
< 0.1%
41.6875 2
 
< 0.1%
87.19916 1
 
< 0.1%
67.15015 1
 
< 0.1%
64.75006 1
 
< 0.1%
Other values (156) 156
 
< 0.1%
(Missing) 561010
> 99.9%
ValueCountFrequency (%)
1.6 1
< 0.1%
1.6667 1
< 0.1%
1.75 1
< 0.1%
2.1 1
< 0.1%
2.55 1
< 0.1%
ValueCountFrequency (%)
590.00531 1
< 0.1%
288.00184 1
< 0.1%
282.31043 1
< 0.1%
264.6 1
< 0.1%
176.49135 1
< 0.1%

peps_issue_price
Real number (ℝ)

Distinct16
Distinct (%)9.9%
Missing561022
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean60.45026235
Minimum12.5
Maximum264.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:35.442490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum12.5
5-th percentile25
Q125
median50
Q3100
95-th percentile100
Maximum264.6
Range252.1
Interquartile range (IQR)75

Descriptive statistics

Standard deviation45.08243139
Coefficient of variation (CV)0.7457772662
Kurtosis8.01891978
Mean60.45026235
Median Absolute Deviation (MAD)25
Skewness2.458429898
Sum9792.9425
Variance2032.42562
MonotonicityNot monotonic
2023-03-30T18:45:35.537340image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
50 58
 
< 0.1%
25 48
 
< 0.1%
100 40
 
< 0.1%
250 4
 
< 0.1%
36.05 1
 
< 0.1%
264.6 1
 
< 0.1%
30.15 1
 
< 0.1%
26.25 1
 
< 0.1%
12.5 1
 
< 0.1%
31.58 1
 
< 0.1%
Other values (6) 6
 
< 0.1%
(Missing) 561022
> 99.9%
ValueCountFrequency (%)
12.5 1
 
< 0.1%
22.25 1
 
< 0.1%
22.5 1
 
< 0.1%
25 48
< 0.1%
26.25 1
 
< 0.1%
ValueCountFrequency (%)
264.6 1
 
< 0.1%
250 4
 
< 0.1%
113.75 1
 
< 0.1%
100 40
< 0.1%
56.6875 1
 
< 0.1%

percs_max_payoff
Real number (ℝ)

Distinct3
Distinct (%)100.0%
Missing561181
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean43.89
Minimum27.08
Maximum63.34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:35.621611image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum27.08
5-th percentile28.497
Q134.165
median41.25
Q352.295
95-th percentile61.131
Maximum63.34
Range36.26
Interquartile range (IQR)18.13

Descriptive statistics

Standard deviation18.27359023
Coefficient of variation (CV)0.4163497433
Kurtosisnan
Mean43.89
Median Absolute Deviation (MAD)14.17
Skewness0.6365494006
Sum131.67
Variance333.9241
MonotonicityNot monotonic
2023-03-30T18:45:35.698171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
63.34 1
 
< 0.1%
27.08 1
 
< 0.1%
41.25 1
 
< 0.1%
(Missing) 561181
> 99.9%
ValueCountFrequency (%)
27.08 1
< 0.1%
41.25 1
< 0.1%
63.34 1
< 0.1%
ValueCountFrequency (%)
63.34 1
< 0.1%
41.25 1
< 0.1%
27.08 1
< 0.1%

dated_date
Categorical

HIGH CARDINALITY  MISSING 

Distinct10783
Distinct (%)2.0%
Missing17777
Missing (%)3.2%
Memory size4.3 MiB
2021-09-30
 
634
2021-06-30
 
606
2022-02-28
 
570
2020-02-28
 
564
2021-01-29
 
537
Other values (10778)
540496 

Unique

Unique1283 ?
Unique (%)0.2%

Sample

1st row1989-11-01
2nd row1993-10-15
3rd row1994-01-14
4th row1994-08-02
5th row1993-05-15

Common Values

ValueCountFrequency (%)
2021-09-30 634
 
0.1%
2021-06-30 606
 
0.1%
2022-02-28 570
 
0.1%
2020-02-28 564
 
0.1%
2021-01-29 537
 
0.1%
2022-04-29 536
 
0.1%
2020-01-31 536
 
0.1%
2020-09-30 531
 
0.1%
2021-02-26 516
 
0.1%
2016-06-30 515
 
0.1%
Other values (10773) 537862
95.8%
(Missing) 17777
 
3.2%

first_interest_date
Categorical

HIGH CARDINALITY  MISSING 

Distinct12379
Distinct (%)2.8%
Missing114332
Missing (%)20.4%
Memory size4.3 MiB
2004-12-15
 
510
2005-06-15
 
497
2003-12-15
 
493
2004-10-15
 
488
2004-06-15
 
477
Other values (12374)
444387 

Unique

Unique921 ?
Unique (%)0.2%

Sample

1st row1990-05-01
2nd row1994-04-15
3rd row1995-02-01
4th row1993-11-15
5th row1994-04-15

Common Values

ValueCountFrequency (%)
2004-12-15 510
 
0.1%
2005-06-15 497
 
0.1%
2003-12-15 493
 
0.1%
2004-10-15 488
 
0.1%
2004-06-15 477
 
0.1%
2004-01-15 475
 
0.1%
2004-09-15 472
 
0.1%
2005-12-15 452
 
0.1%
2005-09-15 452
 
0.1%
2005-03-15 452
 
0.1%
Other values (12369) 442084
78.8%
(Missing) 114332
 
20.4%

interest_frequency
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing1435
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean4.595409728
Minimum-1
Maximum99
Zeros105541
Zeros (%)18.8%
Negative174
Negative (%)< 0.1%
Memory size4.3 MiB
2023-03-30T18:45:35.793144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q12
median2
Q34
95-th percentile12
Maximum99
Range100
Interquartile range (IQR)2

Descriptive statistics

Standard deviation12.0995973
Coefficient of variation (CV)2.632974646
Kurtosis52.05334021
Mean4.595409728
Median Absolute Deviation (MAD)2
Skewness7.065558098
Sum2572276
Variance146.4002548
MonotonicityNot monotonic
2023-03-30T18:45:35.882858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2 273178
48.7%
0 105541
 
18.8%
4 100813
 
18.0%
12 63957
 
11.4%
99 8312
 
1.5%
1 5918
 
1.1%
14 1216
 
0.2%
15 574
 
0.1%
-1 174
 
< 0.1%
13 46
 
< 0.1%
(Missing) 1435
 
0.3%
ValueCountFrequency (%)
-1 174
 
< 0.1%
0 105541
 
18.8%
1 5918
 
1.1%
2 273178
48.7%
4 100813
 
18.0%
ValueCountFrequency (%)
99 8312
1.5%
16 20
 
< 0.1%
15 574
 
0.1%
14 1216
 
0.2%
13 46
 
< 0.1%

coupon
Real number (ℝ)

MISSING  ZEROS 

Distinct17678
Distinct (%)3.7%
Missing80086
Missing (%)14.3%
Infinite0
Infinite (%)0.0%
Mean3.906022517
Minimum-0.80271
Maximum97.29865
Zeros111965
Zeros (%)20.0%
Negative5
Negative (%)< 0.1%
Memory size4.3 MiB
2023-03-30T18:45:36.003494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.80271
5-th percentile0
Q10.32
median3.25
Q36
95-th percentile10.625
Maximum97.29865
Range98.10136
Interquartile range (IQR)5.68

Descriptive statistics

Standard deviation3.873341721
Coefficient of variation (CV)0.9916332288
Kurtosis16.27480277
Mean3.906022517
Median Absolute Deviation (MAD)2.8
Skewness2.01254556
Sum1879179.621
Variance15.00277609
MonotonicityNot monotonic
2023-03-30T18:45:36.135283image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 111965
 
20.0%
5 8299
 
1.5%
2 7443
 
1.3%
3 6953
 
1.2%
6 6931
 
1.2%
4 6814
 
1.2%
1 5393
 
1.0%
5.5 4754
 
0.8%
7 4630
 
0.8%
5.25 4323
 
0.8%
Other values (17668) 313593
55.9%
(Missing) 80086
 
14.3%
ValueCountFrequency (%)
-0.80271 1
< 0.1%
-0.75 1
< 0.1%
-0.375 1
< 0.1%
-0.125 1
< 0.1%
-0.03322 1
< 0.1%
ValueCountFrequency (%)
97.29865 1
 
< 0.1%
94.78302 1
 
< 0.1%
93.84754 7
< 0.1%
93.26785 1
 
< 0.1%
90.28048 1
 
< 0.1%

pay_in_kind
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing118847
Missing (%)21.2%
Memory size4.3 MiB
N
441800 
Y
 
537

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 441800
78.7%
Y 537
 
0.1%
(Missing) 118847
 
21.2%

Common Values (Plot)

2023-03-30T18:45:36.257280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

pay_in_kind_exp_date
Categorical

HIGH CARDINALITY  MISSING 

Distinct244
Distinct (%)73.7%
Missing560853
Missing (%)99.9%
Memory size4.3 MiB
2003-05-15
 
7
2003-02-15
 
4
2018-03-15
 
4
2016-02-15
 
4
2011-07-15
 
4
Other values (239)
308 

Unique

Unique182 ?
Unique (%)55.0%

Sample

1st row1991-12-31
2nd row1998-07-15
3rd row1991-04-06
4th row1993-10-01
5th row1995-08-15

Common Values

ValueCountFrequency (%)
2003-05-15 7
 
< 0.1%
2003-02-15 4
 
< 0.1%
2018-03-15 4
 
< 0.1%
2016-02-15 4
 
< 0.1%
2011-07-15 4
 
< 0.1%
2012-11-01 4
 
< 0.1%
2018-08-15 3
 
< 0.1%
2018-06-15 3
 
< 0.1%
2016-10-15 3
 
< 0.1%
2015-11-01 3
 
< 0.1%
Other values (234) 292
 
0.1%
(Missing) 560853
99.9%
Distinct13
Distinct (%)< 0.1%
Missing6
Missing (%)< 0.1%
Memory size4.3 MiB
N
402241 
F
116453 
U
 
34371
CFFL
 
5868
R
 
618
Other values (8)
 
1627

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 402241
71.7%
F 116453
 
20.8%
U 34371
 
6.1%
CFFL 5868
 
1.0%
R 618
 
0.1%
CFFI 582
 
0.1%
D 457
 
0.1%
T 359
 
0.1%
C 105
 
< 0.1%
S 62
 
< 0.1%
Other values (3) 62
 
< 0.1%

day_count_basis
Categorical

Distinct5
Distinct (%)< 0.1%
Missing4334
Missing (%)0.8%
Memory size4.3 MiB
30/360
502109 
ACT/360
 
42211
ACT/ACT
 
9636
ACT/365
 
2871
ACT/366
 
23

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30/360
2nd row30/360
3rd row30/360
4th row30/360
5th row30/360

Common Values

ValueCountFrequency (%)
30/360 502109
89.5%
ACT/360 42211
 
7.5%
ACT/ACT 9636
 
1.7%
ACT/365 2871
 
0.5%
ACT/366 23
 
< 0.1%
(Missing) 4334
 
0.8%

Common Values (Plot)

2023-03-30T18:45:36.353952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

last_interest_date
Categorical

HIGH CARDINALITY  MISSING 

Distinct15403
Distinct (%)3.9%
Missing163278
Missing (%)29.1%
Memory size4.3 MiB
2008-09-30
 
337
2008-06-30
 
337
2008-06-15
 
299
2008-02-28
 
296
2008-04-30
 
295
Other values (15398)
396342 

Unique

Unique2218 ?
Unique (%)0.6%

Sample

1st row2001-05-01
2nd row2003-04-15
3rd row2009-02-01
4th row2022-11-15
5th row2093-04-15

Common Values

ValueCountFrequency (%)
2008-09-30 337
 
0.1%
2008-06-30 337
 
0.1%
2008-06-15 299
 
0.1%
2008-02-28 296
 
0.1%
2008-04-30 295
 
0.1%
2007-08-15 293
 
0.1%
2010-09-15 282
 
0.1%
2008-02-15 282
 
0.1%
2014-06-15 277
 
< 0.1%
2011-12-15 276
 
< 0.1%
Other values (15393) 394932
70.4%
(Missing) 163278
29.1%

next_interest_date
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing561184
Missing (%)100.0%
Memory size4.3 MiB

currency
Categorical

IMBALANCE  MISSING 

Distinct46
Distinct (%)0.7%
Missing554471
Missing (%)98.8%
Memory size4.3 MiB
EUR
2651 
CAD
2080 
GBP
634 
JPY
295 
AUD
 
121
Other values (41)
932 

Unique

Unique8 ?
Unique (%)0.1%

Sample

1st rowCAD
2nd rowCAD
3rd rowCAD
4th rowCAD
5th rowCAD

Common Values

ValueCountFrequency (%)
EUR 2651
 
0.5%
CAD 2080
 
0.4%
GBP 634
 
0.1%
JPY 295
 
0.1%
AUD 121
 
< 0.1%
ZAR 109
 
< 0.1%
NOK 90
 
< 0.1%
PLN 89
 
< 0.1%
CZK 87
 
< 0.1%
CHF 77
 
< 0.1%
Other values (36) 480
 
0.1%
(Missing) 554471
98.8%

amt_offered
Real number (ℝ)

MISSING  SKEWED 

Distinct318
Distinct (%)4.7%
Missing554471
Missing (%)98.8%
Infinite0
Infinite (%)0.0%
Mean1552047.583
Minimum-200
Maximum2000000000
Zeros5488
Zeros (%)1.0%
Negative1
Negative (%)< 0.1%
Memory size4.3 MiB
2023-03-30T18:45:36.474144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-200
5-th percentile0
Q10
median0
Q30
95-th percentile1000000
Maximum2000000000
Range2000000200
Interquartile range (IQR)0

Descriptive statistics

Standard deviation36777154.27
Coefficient of variation (CV)23.69589352
Kurtosis2494.311196
Mean1552047.583
Median Absolute Deviation (MAD)0
Skewness48.4082822
Sum1.041889543 × 1010
Variance1.352559076 × 1015
MonotonicityNot monotonic
2023-03-30T18:45:36.615435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5488
 
1.0%
500000 174
 
< 0.1%
1000000 107
 
< 0.1%
750000 75
 
< 0.1%
600000 54
 
< 0.1%
300000 46
 
< 0.1%
250000 41
 
< 0.1%
1250000 38
 
< 0.1%
200000 35
 
< 0.1%
700000 28
 
< 0.1%
Other values (308) 627
 
0.1%
(Missing) 554471
98.8%
ValueCountFrequency (%)
-200 1
 
< 0.1%
0 5488
1.0%
4000 1
 
< 0.1%
4554 1
 
< 0.1%
4900 1
 
< 0.1%
ValueCountFrequency (%)
2000000000 1
< 0.1%
1924515000 1
< 0.1%
1000000000 1
< 0.1%
354074000 1
< 0.1%
225523000 1
< 0.1%

conversion_rate
Real number (ℝ)

Distinct33
Distinct (%)68.8%
Missing561136
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean971099.0332
Minimum0
Maximum20000000
Zeros14
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:36.744115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.1386
Q37632.5
95-th percentile4720000
Maximum20000000
Range20000000
Interquartile range (IQR)7632.5

Descriptive statistics

Standard deviation3729777.822
Coefficient of variation (CV)3.84078008
Kurtosis19.60173844
Mean971099.0332
Median Absolute Deviation (MAD)1.1386
Skewness4.427216553
Sum46612753.59
Variance1.39112426 × 1013
MonotonicityNot monotonic
2023-03-30T18:45:36.858203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 14
 
< 0.1%
300000 2
 
< 0.1%
1.1386 2
 
< 0.1%
9980 1
 
< 0.1%
5000000 1
 
< 0.1%
30000 1
 
< 0.1%
4200000 1
 
< 0.1%
100000 1
 
< 0.1%
105.3 1
 
< 0.1%
16000000 1
 
< 0.1%
Other values (23) 23
 
< 0.1%
(Missing) 561136
> 99.9%
ValueCountFrequency (%)
0 14
< 0.1%
0.7285 1
 
< 0.1%
0.7329 1
 
< 0.1%
0.75 1
 
< 0.1%
0.8796 1
 
< 0.1%
ValueCountFrequency (%)
20000000 1
< 0.1%
16000000 1
< 0.1%
5000000 1
< 0.1%
4200000 1
< 0.1%
500000 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing515432
Missing (%)91.8%
Memory size4.3 MiB
Y
35649 
N
10103 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowY
3rd rowY
4th rowY
5th rowY

Common Values

ValueCountFrequency (%)
Y 35649
 
6.4%
N 10103
 
1.8%
(Missing) 515432
91.8%

Common Values (Plot)

2023-03-30T18:45:36.972388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

dividends_related_payments_is
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing515432
Missing (%)91.8%
Memory size4.3 MiB
N
41245 
Y
4507 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowY
5th rowY

Common Values

ValueCountFrequency (%)
N 41245
 
7.3%
Y 4507
 
0.8%
(Missing) 515432
91.8%

Common Values (Plot)

2023-03-30T18:45:37.053953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

funded_debt_is
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing515432
Missing (%)91.8%
Memory size4.3 MiB
N
45388 
Y
 
364

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 45388
 
8.1%
Y 364
 
0.1%
(Missing) 515432
91.8%

Common Values (Plot)

2023-03-30T18:45:37.133737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

indebtedness_is
Categorical

Distinct2
Distinct (%)< 0.1%
Missing515432
Missing (%)91.8%
Memory size4.3 MiB
N
35192 
Y
10560 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowY
4th rowY
5th rowY

Common Values

ValueCountFrequency (%)
N 35192
 
6.3%
Y 10560
 
1.9%
(Missing) 515432
91.8%

Common Values (Plot)

2023-03-30T18:45:37.212113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

investments
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing515432
Missing (%)91.8%
Memory size4.3 MiB
N
44860 
Y
 
892

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 44860
 
8.0%
Y 892
 
0.2%
(Missing) 515432
91.8%

Common Values (Plot)

2023-03-30T18:45:37.292096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

liens_is
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing515432
Missing (%)91.8%
Memory size4.3 MiB
N
42161 
Y
 
3591

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowY
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 42161
 
7.5%
Y 3591
 
0.6%
(Missing) 515432
91.8%

Common Values (Plot)

2023-03-30T18:45:37.369181image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

maintenance_net_worth
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing515432
Missing (%)91.8%
Memory size4.3 MiB
N
45010 
Y
 
742

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 45010
 
8.0%
Y 742
 
0.1%
(Missing) 515432
91.8%

Common Values (Plot)

2023-03-30T18:45:37.861078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing515432
Missing (%)91.8%
Memory size4.3 MiB
N
37329 
Y
8423 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowY
4th rowY
5th rowY

Common Values

ValueCountFrequency (%)
N 37329
 
6.7%
Y 8423
 
1.5%
(Missing) 515432
91.8%

Common Values (Plot)

2023-03-30T18:45:37.936198image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing515432
Missing (%)91.8%
Memory size4.3 MiB
N
32224 
Y
13528 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowY
3rd rowN
4th rowY
5th rowY

Common Values

ValueCountFrequency (%)
N 32224
 
5.7%
Y 13528
 
2.4%
(Missing) 515432
91.8%

Common Values (Plot)

2023-03-30T18:45:38.013958image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

sale_assets
Categorical

Distinct2
Distinct (%)< 0.1%
Missing515432
Missing (%)91.8%
Memory size4.3 MiB
Y
35453 
N
10299 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowY
3rd rowY
4th rowY
5th rowY

Common Values

ValueCountFrequency (%)
Y 35453
 
6.3%
N 10299
 
1.8%
(Missing) 515432
91.8%

Common Values (Plot)

2023-03-30T18:45:38.091317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

senior_debt_issuance
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing515432
Missing (%)91.8%
Memory size4.3 MiB
N
45491 
Y
 
261

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 45491
 
8.1%
Y 261
 
< 0.1%
(Missing) 515432
91.8%

Common Values (Plot)

2023-03-30T18:45:38.167794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

stock_issuance_issuer
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing515432
Missing (%)91.8%
Memory size4.3 MiB
N
44729 
Y
 
1023

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 44729
 
8.0%
Y 1023
 
0.2%
(Missing) 515432
91.8%

Common Values (Plot)

2023-03-30T18:45:38.240240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

stock_transfer_sale_disp
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing515432
Missing (%)91.8%
Memory size4.3 MiB
N
44061 
Y
 
1691

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 44061
 
7.9%
Y 1691
 
0.3%
(Missing) 515432
91.8%

Common Values (Plot)

2023-03-30T18:45:38.314682image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

subordinated_debt_issuance
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing515432
Missing (%)91.8%
Memory size4.3 MiB
N
44486 
Y
 
1266

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowY

Common Values

ValueCountFrequency (%)
N 44486
 
7.9%
Y 1266
 
0.2%
(Missing) 515432
91.8%

Common Values (Plot)

2023-03-30T18:45:38.386701image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing515432
Missing (%)91.8%
Memory size4.3 MiB
N
37226 
Y
8526 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowY
4th rowY
5th rowY

Common Values

ValueCountFrequency (%)
N 37226
 
6.6%
Y 8526
 
1.5%
(Missing) 515432
91.8%

Common Values (Plot)

2023-03-30T18:45:38.463987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

net_earnings_test_issuance
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing515432
Missing (%)91.8%
Memory size4.3 MiB
N
44979 
Y
 
773

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 44979
 
8.0%
Y 773
 
0.1%
(Missing) 515432
91.8%

Common Values (Plot)

2023-03-30T18:45:38.539151image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

fixed_charge_coverage_is
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing515432
Missing (%)91.8%
Memory size4.3 MiB
N
41627 
Y
 
4125

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 41627
 
7.4%
Y 4125
 
0.7%
(Missing) 515432
91.8%

Common Values (Plot)

2023-03-30T18:45:38.613592image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

leverage_test_is
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing515432
Missing (%)91.8%
Memory size4.3 MiB
N
45715 
Y
 
37

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 45715
 
8.1%
Y 37
 
< 0.1%
(Missing) 515432
91.8%

Common Values (Plot)

2023-03-30T18:45:38.689321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

issuer_id_affected
Real number (ℝ)

Distinct602
Distinct (%)51.9%
Missing560023
Missing (%)99.8%
Infinite0
Infinite (%)0.0%
Mean11956.29113
Minimum45
Maximum38239
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:38.781486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum45
5-th percentile875
Q12481
median5738
Q323123
95-th percentile34316
Maximum38239
Range38194
Interquartile range (IQR)20642

Descriptive statistics

Standard deviation12005.27018
Coefficient of variation (CV)1.004096509
Kurtosis-0.8599613964
Mean11956.29113
Median Absolute Deviation (MAD)4269
Skewness0.8563850647
Sum13881254
Variance144126512.1
MonotonicityNot monotonic
2023-03-30T18:45:38.907423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2313 44
 
< 0.1%
3185 33
 
< 0.1%
5144 19
 
< 0.1%
1007 18
 
< 0.1%
11758 17
 
< 0.1%
6178 15
 
< 0.1%
1496 9
 
< 0.1%
1898 9
 
< 0.1%
35258 8
 
< 0.1%
34061 8
 
< 0.1%
Other values (592) 981
 
0.2%
(Missing) 560023
99.8%
ValueCountFrequency (%)
45 2
 
< 0.1%
124 1
 
< 0.1%
168 1
 
< 0.1%
219 1
 
< 0.1%
237 5
< 0.1%
ValueCountFrequency (%)
38239 2
< 0.1%
36576 1
< 0.1%
36375 1
< 0.1%
36200 1
< 0.1%
36146 1
< 0.1%

filing_date
Categorical

HIGH CARDINALITY  MISSING 

Distinct460
Distinct (%)39.6%
Missing560023
Missing (%)99.8%
Memory size4.3 MiB
2002-01-22
 
44
2001-04-06
 
33
2002-07-21
 
32
2001-03-07
 
30
2002-05-08
 
23
Other values (455)
999 

Unique

Unique242 ?
Unique (%)20.8%

Sample

1st row1998-09-28
2nd row1999-06-25
3rd row1999-12-01
4th row1996-01-05
5th row1996-01-05

Common Values

ValueCountFrequency (%)
2002-01-22 44
 
< 0.1%
2001-04-06 33
 
< 0.1%
2002-07-21 32
 
< 0.1%
2001-03-07 30
 
< 0.1%
2002-05-08 23
 
< 0.1%
2001-07-16 22
 
< 0.1%
2003-09-14 17
 
< 0.1%
2003-06-20 16
 
< 0.1%
2002-12-18 13
 
< 0.1%
2001-10-01 13
 
< 0.1%
Other values (450) 918
 
0.2%
(Missing) 560023
99.8%

settlement
Categorical

HIGH CARDINALITY  MISSING 

Distinct541
Distinct (%)48.9%
Missing560077
Missing (%)99.8%
Memory size4.3 MiB
LIQUIDATION.
 
47
HOLDERS WILL REC APPX 29% OF THE STOCK OF THE REORG KMART.
 
43
100% INITIAL EQUITY OF NTL UK & IRELAND, APPX 86.5% NTL EUROCO.
 
19
SR NT $400MM W/ AN INT RATE OF 3 MO AVE LIBOR + 3% & $500MM 11%.
 
18
BOND HOLDERS WILL GET .36CENTS OF NEW DEBT & EQUITY FOR EACH $.
 
17
Other values (536)
963 

Unique

Unique367 ?
Unique (%)33.2%

Sample

1st row$1,000 PRIN AMT OF NT, HLDR WILL REC $10.213 IN CASH PRIN & INT.
2nd rowHLDG'S REC 45% FACE AMT, 6% NEW NOTES & STOCK IN NEW COMPANY.
3rd rowUS GOVN'MT GUARANTEED SHIP FINANCING. SHIP CHG'D GOVN'MT AGENCY.
4th rowNO SETTLEMENT
5th rowCANCELED / WILL RECEIVE NEW COMMON STOCK AND WARRANTS

Common Values

ValueCountFrequency (%)
LIQUIDATION. 47
 
< 0.1%
HOLDERS WILL REC APPX 29% OF THE STOCK OF THE REORG KMART. 43
 
< 0.1%
100% INITIAL EQUITY OF NTL UK & IRELAND, APPX 86.5% NTL EUROCO. 19
 
< 0.1%
SR NT $400MM W/ AN INT RATE OF 3 MO AVE LIBOR + 3% & $500MM 11%. 18
 
< 0.1%
BOND HOLDERS WILL GET .36CENTS OF NEW DEBT & EQUITY FOR EACH $. 17
 
< 0.1%
RESTRUCTURED INTO NEW SR NOTES OF FINOVA. 17
 
< 0.1%
CREDITORS WILL REC A COMBINATION OF CASH AND NEW NTS. 16
 
< 0.1%
SALE OF ASSETS. 16
 
< 0.1%
NO INFORMATION. 15
 
< 0.1%
PROPOSED $568MM SALE OF ALL COMPANY ASSETS TO KPNQWEST NV. 11
 
< 0.1%
Other values (531) 888
 
0.2%
(Missing) 560077
99.8%

other_sec_type
Categorical

Distinct6
Distinct (%)20.0%
Missing561154
Missing (%)> 99.9%
Memory size4.3 MiB
PC
13 
CS
10 
CSB
DEB
 
1
PS
 
1

Unique

Unique3 ?
Unique (%)10.0%

Sample

1st rowCSB
2nd rowCS
3rd rowCS
4th rowCS
5th rowCS

Common Values

ValueCountFrequency (%)
PC 13
 
< 0.1%
CS 10
 
< 0.1%
CSB 4
 
< 0.1%
DEB 1
 
< 0.1%
PS 1
 
< 0.1%
CSA 1
 
< 0.1%
(Missing) 561154
> 99.9%

Common Values (Plot)

2023-03-30T18:45:39.038401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

other_sec_issuer
Categorical

Distinct50
Distinct (%)64.9%
Missing561107
Missing (%)> 99.9%
Memory size4.3 MiB
COMMON STOCK
19 
UNITED MEXICAN STATES
TEXAS UTILITIES CO
 
2
PPL CORPORATION
 
2
ONO FINANCE PLC
 
2
Other values (45)
46 

Unique

Unique44 ?
Unique (%)57.1%

Sample

1st rowAFFILIATED NEWSPAPERS
2nd rowCOMMON STOCK
3rd rowAFCA COMMON STOCK
4th rowCOMMON STOCK
5th rowCOMMON STOCK

Common Values

ValueCountFrequency (%)
COMMON STOCK 19
 
< 0.1%
UNITED MEXICAN STATES 6
 
< 0.1%
TEXAS UTILITIES CO 2
 
< 0.1%
PPL CORPORATION 2
 
< 0.1%
ONO FINANCE PLC 2
 
< 0.1%
SMITTYS SUPERMARKET INC. COMMON STOCK CL 2
 
< 0.1%
KAUFMAN & BROAD HOME 1
 
< 0.1%
LINCOLN NATIONAL CORP 1
 
< 0.1%
MONSANTO CO 1
 
< 0.1%
ARGENTINAL REP 1
 
< 0.1%
Other values (40) 40
 
< 0.1%
(Missing) 561107
> 99.9%

sec_cusip
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing561173
Missing (%)> 99.9%
Memory size4.3 MiB

quantity
Real number (ℝ)

Distinct49
Distinct (%)62.8%
Missing561106
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean1608059.834
Minimum0
Maximum20000000
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:39.152934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4538
Q15
median20
Q312675
95-th percentile11435144.95
Maximum20000000
Range20000000
Interquartile range (IQR)12670

Descriptive statistics

Standard deviation4488069.351
Coefficient of variation (CV)2.790984051
Kurtosis9.19406511
Mean1608059.834
Median Absolute Deviation (MAD)19.904
Skewness3.129573308
Sum125428667.1
Variance2.01427665 × 1013
MonotonicityNot monotonic
2023-03-30T18:45:39.279739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
1 7
 
< 0.1%
2 4
 
< 0.1%
10 4
 
< 0.1%
9000 3
 
< 0.1%
9 3
 
< 0.1%
20 3
 
< 0.1%
0 3
 
< 0.1%
5 3
 
< 0.1%
100 3
 
< 0.1%
2578470 2
 
< 0.1%
Other values (39) 43
 
< 0.1%
(Missing) 561106
> 99.9%
ValueCountFrequency (%)
0 3
< 0.1%
0.192 1
 
< 0.1%
0.5 1
 
< 0.1%
1 7
< 0.1%
1.723 1
 
< 0.1%
ValueCountFrequency (%)
20000000 2
< 0.1%
17500000 1
< 0.1%
16500000 1
< 0.1%
10541347 1
< 0.1%
9769923 1
< 0.1%
Distinct22
Distinct (%)73.3%
Missing561154
Missing (%)> 99.9%
Memory size4.3 MiB
1992-07-01
2000-05-06
2001-05-09
1994-09-15
2001-10-31
 
1
Other values (17)
17 

Unique

Unique18 ?
Unique (%)60.0%

Sample

1st row1994-07-20
2nd row1985-09-30
3rd row1985-07-02
4th row1994-07-13
5th row1994-09-15

Common Values

ValueCountFrequency (%)
1992-07-01 6
 
< 0.1%
2000-05-06 2
 
< 0.1%
2001-05-09 2
 
< 0.1%
1994-09-15 2
 
< 0.1%
2001-10-31 1
 
< 0.1%
2001-06-19 1
 
< 0.1%
2001-03-21 1
 
< 0.1%
2000-08-22 1
 
< 0.1%
2000-06-28 1
 
< 0.1%
2000-06-16 1
 
< 0.1%
Other values (12) 12
 
< 0.1%
(Missing) 561154
> 99.9%
Distinct2
Distinct (%)6.7%
Missing561154
Missing (%)> 99.9%
Memory size4.3 MiB
N
19 
Y
11 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowY
3rd rowN
4th rowY
5th rowN

Common Values

ValueCountFrequency (%)
N 19
 
< 0.1%
Y 11
 
< 0.1%
(Missing) 561154
> 99.9%

Common Values (Plot)

2023-03-30T18:45:39.400112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

market_price
Real number (ℝ)

Distinct29
Distinct (%)49.2%
Missing561125
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean6.473644068
Minimum0
Maximum51
Zeros28
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:39.480854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q310.25
95-th percentile22.4875
Maximum51
Range51
Interquartile range (IQR)10.25

Descriptive statistics

Standard deviation9.729837323
Coefficient of variation (CV)1.502992321
Kurtosis6.966728533
Mean6.473644068
Median Absolute Deviation (MAD)1
Skewness2.2889427
Sum381.945
Variance94.66973434
MonotonicityNot monotonic
2023-03-30T18:45:39.584600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 28
 
< 0.1%
15.41 2
 
< 0.1%
8 2
 
< 0.1%
9.75 2
 
< 0.1%
9.5 1
 
< 0.1%
29.125 1
 
< 0.1%
21.25 1
 
< 0.1%
10.75 1
 
< 0.1%
5.375 1
 
< 0.1%
1.375 1
 
< 0.1%
Other values (19) 19
 
< 0.1%
(Missing) 561125
> 99.9%
ValueCountFrequency (%)
0 28
< 0.1%
0.5 1
 
< 0.1%
1 1
 
< 0.1%
1.375 1
 
< 0.1%
2.5 1
 
< 0.1%
ValueCountFrequency (%)
51 1
< 0.1%
30.875 1
< 0.1%
29.125 1
< 0.1%
21.75 1
< 0.1%
21.25 1
< 0.1%
Distinct29
Distinct (%)43.9%
Missing561118
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean53.35483333
Minimum0
Maximum1156
Zeros35
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:39.685677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q315.8525
95-th percentile294
Maximum1156
Range1156
Interquartile range (IQR)15.8525

Descriptive statistics

Standard deviation160.9600562
Coefficient of variation (CV)3.016784913
Kurtosis34.5378874
Mean53.35483333
Median Absolute Deviation (MAD)0
Skewness5.380021942
Sum3521.419
Variance25908.1397
MonotonicityNot monotonic
2023-03-30T18:45:39.786104image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 35
 
< 0.1%
12.375 2
 
< 0.1%
195 2
 
< 0.1%
15.41 2
 
< 0.1%
200 1
 
< 0.1%
24.73 1
 
< 0.1%
1.459 1
 
< 0.1%
0.4 1
 
< 0.1%
0.24 1
 
< 0.1%
1156 1
 
< 0.1%
Other values (19) 19
 
< 0.1%
(Missing) 561118
> 99.9%
ValueCountFrequency (%)
0 35
< 0.1%
0.067 1
 
< 0.1%
0.24 1
 
< 0.1%
0.353 1
 
< 0.1%
0.4 1
 
< 0.1%
ValueCountFrequency (%)
1156 1
< 0.1%
352.5 1
< 0.1%
323 1
< 0.1%
304.5 1
< 0.1%
262.5 1
< 0.1%

overallotment_expiration_date
Categorical

HIGH CARDINALITY  MISSING 

Distinct3023
Distinct (%)69.1%
Missing556812
Missing (%)99.2%
Memory size4.3 MiB
2020-06-01
 
13
2004-03-11
 
6
2008-01-05
 
6
2003-06-20
 
6
2006-05-12
 
5
Other values (3018)
4336 

Unique

Unique2086 ?
Unique (%)47.7%

Sample

1st row1994-01-14
2nd row1994-02-20
3rd row1992-04-20
4th row1992-04-12
5th row2002-01-15

Common Values

ValueCountFrequency (%)
2020-06-01 13
 
< 0.1%
2004-03-11 6
 
< 0.1%
2008-01-05 6
 
< 0.1%
2003-06-20 6
 
< 0.1%
2006-05-12 5
 
< 0.1%
2021-03-18 5
 
< 0.1%
2017-09-16 5
 
< 0.1%
2006-12-15 5
 
< 0.1%
2003-06-14 5
 
< 0.1%
2003-06-29 5
 
< 0.1%
Other values (3013) 4311
 
0.8%
(Missing) 556812
99.2%

exercised
Categorical

Distinct2
Distinct (%)< 0.1%
Missing557173
Missing (%)99.3%
Memory size4.3 MiB
Y
3217 
N
794 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowY
3rd rowY
4th rowY
5th rowY

Common Values

ValueCountFrequency (%)
Y 3217
 
0.6%
N 794
 
0.1%
(Missing) 557173
99.3%

Common Values (Plot)

2023-03-30T18:45:39.893229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

exercised_date
Categorical

HIGH CARDINALITY  MISSING 

Distinct2388
Distinct (%)75.4%
Missing558015
Missing (%)99.4%
Memory size4.3 MiB
2003-07-30
 
6
2021-03-16
 
6
2003-07-02
 
6
2009-10-01
 
6
1999-01-14
 
5
Other values (2383)
3140 

Unique

Unique1798 ?
Unique (%)56.7%

Sample

1st row1994-02-20
2nd row1992-04-20
3rd row1992-04-12
4th row1995-01-04
5th row1992-07-05

Common Values

ValueCountFrequency (%)
2003-07-30 6
 
< 0.1%
2021-03-16 6
 
< 0.1%
2003-07-02 6
 
< 0.1%
2009-10-01 6
 
< 0.1%
1999-01-14 5
 
< 0.1%
2003-06-30 5
 
< 0.1%
2019-10-31 5
 
< 0.1%
2003-06-23 5
 
< 0.1%
2020-05-22 5
 
< 0.1%
2003-06-16 5
 
< 0.1%
Other values (2378) 3115
 
0.6%
(Missing) 558015
99.4%

amount
Real number (ℝ)

MISSING  SKEWED 

Distinct654
Distinct (%)15.3%
Missing556902
Missing (%)99.2%
Infinite0
Infinite (%)0.0%
Mean167573.4081
Minimum0
Maximum210245000
Zeros7
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:39.996458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1650
Q111250
median25000
Q350000
95-th percentile150000
Maximum210245000
Range210245000
Interquartile range (IQR)38750

Descriptive statistics

Standard deviation3660998.14
Coefficient of variation (CV)21.84713065
Kurtosis2604.658668
Mean167573.4081
Median Absolute Deviation (MAD)17500
Skewness47.88437067
Sum717549333.4
Variance1.340290738 × 1013
MonotonicityNot monotonic
2023-03-30T18:45:40.115472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15000 311
 
0.1%
25000 303
 
0.1%
50000 252
 
< 0.1%
30000 223
 
< 0.1%
75000 180
 
< 0.1%
22500 136
 
< 0.1%
45000 134
 
< 0.1%
10000 121
 
< 0.1%
37500 112
 
< 0.1%
20000 109
 
< 0.1%
Other values (644) 2401
 
0.4%
(Missing) 556902
99.2%
ValueCountFrequency (%)
0 7
< 0.1%
12 1
 
< 0.1%
22 1
 
< 0.1%
23 2
 
< 0.1%
30 1
 
< 0.1%
ValueCountFrequency (%)
210245000 1
< 0.1%
82500000 1
< 0.1%
48750000 1
< 0.1%
37500000 1
< 0.1%
30000000 1
< 0.1%

notification_period
Categorical

HIGH CARDINALITY  MISSING 

Distinct462
Distinct (%)22.2%
Missing559100
Missing (%)99.6%
Memory size4.3 MiB
1-20 BUSINESS DAYS
483 
20 BUSINESS DAYS
192 
30 DAYS
179 
30-60 DAYS
 
100
20 DAYS
 
67
Other values (457)
1063 

Unique

Unique315 ?
Unique (%)15.1%

Sample

1st row20 DAYS
2nd row30 DAYS
3rd row07/15 - 08/01
4th row30-60 DAYS
5th row12/16 - 01/15

Common Values

ValueCountFrequency (%)
1-20 BUSINESS DAYS 483
 
0.1%
20 BUSINESS DAYS 192
 
< 0.1%
30 DAYS 179
 
< 0.1%
30-60 DAYS 100
 
< 0.1%
20 DAYS 67
 
< 0.1%
SEE FOOTNOTE 31
 
< 0.1%
0-20 BUSINESS DAYS 29
 
< 0.1%
5-20 BUSINESS DAYS 29
 
< 0.1%
30 BUSINESS DAYS 24
 
< 0.1%
15 30 23
 
< 0.1%
Other values (452) 927
 
0.2%
(Missing) 559100
99.6%

next_put_date
Categorical

HIGH CARDINALITY  MISSING 

Distinct153
Distinct (%)63.5%
Missing560943
Missing (%)> 99.9%
Memory size4.3 MiB
2023-11-04
 
7
2022-12-15
 
6
2022-11-27
 
6
2024-10-26
 
6
2022-11-15
 
5
Other values (148)
211 

Unique

Unique109 ?
Unique (%)45.2%

Sample

1st row2022-08-15
2nd row2022-09-15
3rd row2022-09-01
4th row2023-01-01
5th row2023-08-01

Common Values

ValueCountFrequency (%)
2023-11-04 7
 
< 0.1%
2022-12-15 6
 
< 0.1%
2022-11-27 6
 
< 0.1%
2024-10-26 6
 
< 0.1%
2022-11-15 5
 
< 0.1%
2022-09-15 5
 
< 0.1%
2022-08-15 5
 
< 0.1%
2023-12-21 4
 
< 0.1%
2023-02-07 4
 
< 0.1%
2023-02-15 4
 
< 0.1%
Other values (143) 189
 
< 0.1%
(Missing) 560943
> 99.9%

next_put_price
Real number (ℝ)

Distinct13
Distinct (%)5.3%
Missing560941
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean99.37262934
Minimum48.47231
Maximum110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:40.215401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum48.47231
5-th percentile98
Q1100
median100
Q3100
95-th percentile100
Maximum110
Range61.52769
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.762159893
Coefficient of variation (CV)0.03785911591
Kurtosis145.390164
Mean99.37262934
Median Absolute Deviation (MAD)0
Skewness-11.22781629
Sum24147.54893
Variance14.15384706
MonotonicityNot monotonic
2023-03-30T18:45:40.294599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
100 194
 
< 0.1%
98 35
 
< 0.1%
99 4
 
< 0.1%
98.14 1
 
< 0.1%
76.1 1
 
< 0.1%
99.5 1
 
< 0.1%
99.625 1
 
< 0.1%
99.125 1
 
< 0.1%
99.75 1
 
< 0.1%
96.81462 1
 
< 0.1%
Other values (3) 3
 
< 0.1%
(Missing) 560941
> 99.9%
ValueCountFrequency (%)
48.47231 1
 
< 0.1%
76.1 1
 
< 0.1%
94.022 1
 
< 0.1%
96.81462 1
 
< 0.1%
98 35
< 0.1%
ValueCountFrequency (%)
110 1
 
< 0.1%
100 194
< 0.1%
99.75 1
 
< 0.1%
99.625 1
 
< 0.1%
99.5 1
 
< 0.1%

borrowing_restricted
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing515432
Missing (%)91.8%
Memory size4.3 MiB
N
45688 
Y
 
64

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 45688
 
8.1%
Y 64
 
< 0.1%
(Missing) 515432
91.8%

Common Values (Plot)

2023-03-30T18:45:40.387114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing515432
Missing (%)91.8%
Memory size4.3 MiB
N
38162 
Y
7590 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowY
4th rowY
5th rowY

Common Values

ValueCountFrequency (%)
N 38162
 
6.8%
Y 7590
 
1.4%
(Missing) 515432
91.8%

Common Values (Plot)

2023-03-30T18:45:40.461693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

funded_debt_sub
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing515432
Missing (%)91.8%
Memory size4.3 MiB
N
45363 
Y
 
389

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 45363
 
8.1%
Y 389
 
0.1%
(Missing) 515432
91.8%

Common Values (Plot)

2023-03-30T18:45:40.537014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

indebtedness_sub
Categorical

Distinct2
Distinct (%)< 0.1%
Missing515432
Missing (%)91.8%
Memory size4.3 MiB
N
35246 
Y
10506 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowY
4th rowY
5th rowY

Common Values

ValueCountFrequency (%)
N 35246
 
6.3%
Y 10506
 
1.9%
(Missing) 515432
91.8%

Common Values (Plot)

2023-03-30T18:45:40.611869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

stock_issuance
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing515432
Missing (%)91.8%
Memory size4.3 MiB
N
42584 
Y
 
3168

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowY
5th rowY

Common Values

ValueCountFrequency (%)
N 42584
 
7.6%
Y 3168
 
0.6%
(Missing) 515432
91.8%

Common Values (Plot)

2023-03-30T18:45:40.687234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

preferred_stock_issuance
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing515432
Missing (%)91.8%
Memory size4.3 MiB
N
42113 
Y
 
3639

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 42113
 
7.5%
Y 3639
 
0.6%
(Missing) 515432
91.8%

Common Values (Plot)

2023-03-30T18:45:40.761189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

investments_unrestricted_subs
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing515432
Missing (%)91.8%
Memory size4.3 MiB
N
45148 
Y
 
604

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowY
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 45148
 
8.0%
Y 604
 
0.1%
(Missing) 515432
91.8%

Common Values (Plot)

2023-03-30T18:45:40.834132image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

sale_xfer_assets_unrestricted
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing515432
Missing (%)91.8%
Memory size4.3 MiB
N
45615 
Y
 
137

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 45615
 
8.1%
Y 137
 
< 0.1%
(Missing) 515432
91.8%

Common Values (Plot)

2023-03-30T18:45:40.906434image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

subsidiary_redesignation
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing515432
Missing (%)91.8%
Memory size4.3 MiB
N
43499 
Y
 
2253

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 43499
 
7.8%
Y 2253
 
0.4%
(Missing) 515432
91.8%

Common Values (Plot)

2023-03-30T18:45:40.980506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

subsidiary_guarantee
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing515432
Missing (%)91.8%
Memory size4.3 MiB
N
40935 
Y
4817 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowY

Common Values

ValueCountFrequency (%)
N 40935
 
7.3%
Y 4817
 
0.9%
(Missing) 515432
91.8%

Common Values (Plot)

2023-03-30T18:45:41.054622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing515432
Missing (%)91.8%
Memory size4.3 MiB
N
32891 
Y
12861 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowY
3rd rowN
4th rowY
5th rowY

Common Values

ValueCountFrequency (%)
N 32891
 
5.9%
Y 12861
 
2.3%
(Missing) 515432
91.8%

Common Values (Plot)

2023-03-30T18:45:41.131446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

liens_sub
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing515432
Missing (%)91.8%
Memory size4.3 MiB
N
42692 
Y
 
3060

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 42692
 
7.6%
Y 3060
 
0.5%
(Missing) 515432
91.8%

Common Values (Plot)

2023-03-30T18:45:41.206345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

fixed_charge_coverage_sub
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing515432
Missing (%)91.8%
Memory size4.3 MiB
N
41694 
Y
 
4058

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 41694
 
7.4%
Y 4058
 
0.7%
(Missing) 515432
91.8%

Common Values (Plot)

2023-03-30T18:45:41.280468image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

leverage_test_sub
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing515432
Missing (%)91.8%
Memory size4.3 MiB
N
45729 
Y
 
23

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 45729
 
8.1%
Y 23
 
< 0.1%
(Missing) 515432
91.8%

Common Values (Plot)

2023-03-30T18:45:41.355830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

unit_cusip
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing560998
Missing (%)> 99.9%
Memory size4.3 MiB

total_units_offered
Real number (ℝ)

Distinct117
Distinct (%)69.6%
Missing561016
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean957131.5893
Minimum0
Maximum20000000
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:41.449527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile79.2
Q124500
median125000
Q3303500
95-th percentile3867229.6
Maximum20000000
Range20000000
Interquartile range (IQR)279000

Descriptive statistics

Standard deviation3212874.904
Coefficient of variation (CV)3.356774491
Kurtosis22.46410593
Mean957131.5893
Median Absolute Deviation (MAD)114626
Skewness4.713944539
Sum160798107
Variance1.032256515 × 1013
MonotonicityNot monotonic
2023-03-30T18:45:41.578009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000 10
 
< 0.1%
300000 7
 
< 0.1%
0 5
 
< 0.1%
125000 3
 
< 0.1%
6000 3
 
< 0.1%
9000 3
 
< 0.1%
80000 3
 
< 0.1%
225000 3
 
< 0.1%
105000 3
 
< 0.1%
50000 3
 
< 0.1%
Other values (107) 125
 
< 0.1%
(Missing) 561016
> 99.9%
ValueCountFrequency (%)
0 5
< 0.1%
1 1
 
< 0.1%
20 1
 
< 0.1%
35 1
 
< 0.1%
75 1
 
< 0.1%
ValueCountFrequency (%)
20000000 2
< 0.1%
17500000 1
< 0.1%
16500000 1
< 0.1%
14000000 1
< 0.1%
10541347 1
< 0.1%

principal_amt_per_unit
Real number (ℝ)

Distinct28
Distinct (%)18.1%
Missing561029
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean1014.614523
Minimum0
Maximum20000
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:41.699063image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25
Q1307.4935
median1000
Q31000
95-th percentile1000
Maximum20000
Range20000
Interquartile range (IQR)692.5065

Descriptive statistics

Standard deviation1947.507728
Coefficient of variation (CV)1.919455798
Kurtosis64.19708134
Mean1014.614523
Median Absolute Deviation (MAD)0
Skewness7.428510541
Sum157265.251
Variance3792786.351
MonotonicityNot monotonic
2023-03-30T18:45:41.800783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1000 99
 
< 0.1%
50 15
 
< 0.1%
25 9
 
< 0.1%
0 4
 
< 0.1%
10 3
 
< 0.1%
100 2
 
< 0.1%
10000 2
 
< 0.1%
104.987 1
 
< 0.1%
996.13 1
 
< 0.1%
40 1
 
< 0.1%
Other values (18) 18
 
< 0.1%
(Missing) 561029
> 99.9%
ValueCountFrequency (%)
0 4
< 0.1%
10 3
 
< 0.1%
25 9
< 0.1%
40 1
 
< 0.1%
41.5 1
 
< 0.1%
ValueCountFrequency (%)
20000 1
 
< 0.1%
10000 2
 
< 0.1%
5000 1
 
< 0.1%
3750 1
 
< 0.1%
1000 99
< 0.1%
Distinct84
Distinct (%)38.9%
Missing560968
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean426.6437591
Minimum0
Maximum41303
Zeros112
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2023-03-30T18:45:41.925964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3486.915
95-th percentile984.1475
Maximum41303
Range41303
Interquartile range (IQR)486.915

Descriptive statistics

Standard deviation2833.450926
Coefficient of variation (CV)6.641257175
Kurtosis204.1446634
Mean426.6437591
Median Absolute Deviation (MAD)0
Skewness14.11041286
Sum92155.05196
Variance8028444.15
MonotonicityNot monotonic
2023-03-30T18:45:42.047566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 112
 
< 0.1%
50 8
 
< 0.1%
25 6
 
< 0.1%
87.625 2
 
< 0.1%
977.5 2
 
< 0.1%
100 2
 
< 0.1%
945.5 2
 
< 0.1%
805 2
 
< 0.1%
863.93 2
 
< 0.1%
984.59 2
 
< 0.1%
Other values (74) 76
 
< 0.1%
(Missing) 560968
> 99.9%
ValueCountFrequency (%)
0 112
< 0.1%
0.27 1
 
< 0.1%
9.933 1
 
< 0.1%
10 2
 
< 0.1%
23.541 1
 
< 0.1%
ValueCountFrequency (%)
41303 1
< 0.1%
4738.87 1
< 0.1%
1000 2
< 0.1%
998.64 1
< 0.1%
988.73 1
< 0.1%